Is AI going to replace digital marketing?
Imagine trying to sell shoes to everyone watching the same TV channel. That’s how marketing used to be! Now, with the internet and fancy tech tools, things are totally different. Marketers can use social media, search engines, and even phone apps to reach people who might actually be interested in their stuff. This lets them build better relationships with customers. But guess what? Customers are also getting smarter. They can find all sorts of information about products online, so they have more control than ever before. Here’s where things get really cool: Artificial intelligence (AI), basically super-smart computer programs, are starting to play a big role in marketing. AI can do amazing things, like automatically handle boring tasks and analyze mountains of data to understand what customers want. This helps marketers create more interesting and relevant content, like funny ads or personalized recommendations for products you might actually use. However, AI isn’t here to steal all the marketing jobs. It can’t replace the awesome creativity and strategic thinking that humans bring to the table. Humans are still the best at making decisions that consider what’s right and wrong, and figuring out how to reach specific groups of people. So, the future of marketing is a team effort between super-smart AI and creative humans. With this powerful combo, marketers can be even more efficient and effective, reaching the right people with the right message at the right time. Examples of how to use AI is transforming marketing: Content Creation: Generative AI tools like Jasper for text generation and Midjourney for image creation help marketers produce custom content quickly. Whether it’s crafting blog posts, social media captions, or eye-catching visuals, AI streamlines the creative process. For instance, you can use AI to craft a blog post in just 10 minutes or less! It’s all about working smarter, not harder. Data Analysis and Reporting: AI automates complex data analysis and transforms insights into easy-to-understand reports and visualizations. Previously, creating quarterly reports was a marathon task. Now, AI sifts through data, spots trends, and presents it all in sleek formats. ChatSpot, for example, integrates with CRM systems to generate instant progress reports. Research and Inspiration: Imagine hitting a roadblock during campaign brainstorming. AI kick-starts your creativity by providing endless inspiration and research possibilities. It’s like having an AI-powered idea generator at your fingertips, helping you explore new angles and possibilities. Dynamic Pricing: Algorithms adjust prices in real time based on demand, competitor pricing, and other factors. Uber’s ride-sharing app and airline ticketing systems use AI-powered dynamic pricing. Lead Outreach and Customer Support: AI automates lead outreach, schedules meetings, and provides chat-based customer support services. It ensures timely responses and enhances customer experiences. Tailored Product Ads: AI analyzes user behavior and preferences to deliver personalized product recommendations and targeted ads. Think of how Amazon suggests products based on your browsing history or previous purchases. Sentiment Analysis: AI tools analyze social media posts, reviews, and customer feedback to gauge sentiment. Brands can adjust their strategies based on public sentiment and address any negative feedback promptly. Remember, AI doesn’t replace human marketers; it complements their efforts. By leveraging AI, marketers can work more efficiently, make data-driven decisions, and create better experiences for their audiences. AI-Driven Marketing Campaigns Success Stories and Their Outcomes 1. Nutella’s Unique Packaging Design: Nutella, the popular hazelnut spread brand, collaborated with Ogilvy Italia to create 7 million unique packaging designs using AI. By analyzing consumer data, they personalized each jar’s label, resulting in a 25% increase in customer conversions and a 30% boost in revenue. 2. Sephora’s AI-Powered Chatbot: Sephora, a beauty retailer, developed the “Sephora Virtual Artist” chatbot. Customers could virtually try on makeup products and receive personalized recommendations. This AI-driven social media campaign led to increased sales and customer satisfaction. 3. HubSpot’s Data-Driven Insights: HubSpot, a marketing platform, leveraged AI for data analysis and insights. By automating tasks and analyzing vast amounts of data, they optimized resource allocation and improved campaign performance. 4. Adobe’s Customer Engagement: Adobe used AI to enhance customer engagement. Personalized recommendations, dynamic content, and interactive chatbots led to higher levels of engagement and brand loyalty. 5. Salesforce’s AI-Driven CRM: Salesforce integrated AI into its customer relationship management (CRM) system. AI-powered insights helped sales teams make informed decisions and improve customer interactions. These examples demonstrate how AI transforms marketing, making it more effective, personalized, and efficient. Top Brief Blogs: https://www.linkedin.com/pulse/artificial-intelligence-replace-digital-marketers-digiliteco-vk1pe/ https://www.webfx.com/blog/marketing/will-ai-replace-marketing-jobs/ https://neilpatel.com/blog/will-ai-replace-human-marketers/ 11 Artificial Intelligence Examples from Real Brands in 2023 (hubspot.com) Top Research Papers: https://journals.sagepub.com/doi/abs/10.1177/18393349211037684 https://link.springer.com/chapter/10.1007/978-981-99-5354-7_9 https://www.theseus.fi/handle/10024/344493
Role of Artificial Intelligence and Machine Learning in Digital Marketing: Transforming Strategies | Baking AI
Artificial Intelligence (AI) and Machine Learning (ML) are like smart tools that help computers do things that normally need human smarts. They’re changing a lot of industries, including digital marketing. These technologies can help businesses connect better with their customers, make marketing more effective, and get better results. In today’s world, where there’s lots of data and people’s shopping habits are more complicated, AI and ML can help companies understand data better, make marketing more personal, and improve how customers feel about their shopping experience. What is Artificial Intelligence (AI)? Artificial Intelligence (AI) is about making machines smart like humans. It helps them do things like thinking, solving problems, learning, understanding languages, and recognizing things. AI comes in two main types: Narrow AI, which is good at specific tasks, and General AI, which tries to be as smart as humans in many areas. In digital marketing, AI helps by using smart systems to study data, do tasks automatically, and make decisions based on data. With AI, computers can handle lots of customer information, find patterns, and give useful insights. In marketing, AI can help in many ways like understanding customers better, targeting them with personalized messages, creating content, using chatbots, and predicting future trends. What is Machine Learning (ML)? Machine learning is a part of AI that helps machines learn from data and make decisions without being explicitly programmed. It uses algorithms and statistical models to find patterns in data and make predictions or decisions. In digital marketing, machine learning is used to analyze customer data and improve marketing strategies. By understanding trends and customer behavior, it helps in personalizing content, optimizing marketing campaigns, and predicting future actions. Techniques like clustering, classification, regression, and recommendation systems are commonly used in digital marketing to make these improvements. How AI and ML are Transforming Digital Strategies? AI and machine learning are making a big difference in how businesses do digital marketing. Imagine having a super-powered tool that can analyze all your customer information and help you create exactly what each customer wants to see. That’s what AI and machine learning do! With this technology, businesses can design personalized experiences for their customers, target the right people with the right ads, and even answer customer questions with chatbots. This all leads to happier customers and more sales for businesses. But it’s important to use these tools responsibly, which means being careful with customer data and making sure the technology is fair and unbiased. Overall, AI and machine learning are a game-changer for digital marketing, giving businesses a big advantage in today’s competitive world. Advancements in AI and ML for Digital Marketing AI and machine learning (ML) are getting really good at helping marketers! With all the data available these days, AI and ML tools can find important information and trends. This helps marketers understand their customers better, like what content they like and how they prefer to shop. By using this knowledge, marketers can create more relevant experiences for their target audience, which keeps people interested and wanting to buy more. Personalization and Targeted Advertising: Imagine seeing ads and getting recommendations that are actually relevant to you! This is what AI and machine learning help with in marketing. By analyzing information about customers, like what they buy and browse online, these technologies can predict what they might be interested in. This allows companies to show them personalized ads and suggestions, which makes customers happier and more likely to buy something. Targeting the right people with the right message is a powerful way to improve marketing results. Customer Experience Optimization: In the world of business, new tech like AI and ML are making things way better for customers. Companies can now use fancy tools to listen in on what customers are saying online and on social media. This helps them understand what problems customers are having and what they like. With this knowledge, businesses can fix issues, make things smoother for customers, and keep them happy. Research shows that AI chatbots can also be superstars by helping customers in real time and making sure they have a good experience. Ethical Considerations and Challenges: AI and machine learning (ML) can be great tools for digital marketing, but there are some things to keep in mind. These technologies use a lot of data, and if that data isn’t carefully checked, it can lead to unfair targeting or even discrimination. Basically, the AI might learn bad habits from the data it’s given. That’s why it’s important for marketers to use AI and ML in a responsible way, and to be clear about how they’re using it. Impact of AI and ML in Digital Marketing Strategies AI (Artificial Intelligence) and ML (Machine Learning) play a significant role in boosting digital marketing efforts. Here are several ways in which AI and ML contribute to enhancing digital marketing: 1. Data Analysis and Insights: AI and ML algorithms can analyse vast amounts of data quickly and efficiently. By processing data from various sources such as customer interactions, social media, website behaviour, and market trends, AI and ML can extract valuable insights. These insights enable marketers to understand customer preferences, behaviours, and patterns, helping them make data-driven decisions and optimize marketing strategies. 2. Personalization and Targeting: AI and ML enable personalized marketing by leveraging customer data to create tailored experiences. Through predictive analytics, these technologies can determine customer preferences and deliver personalized content, recommendations, and offers. This level of personalization enhances customer engagement, improves customer satisfaction, and increases conversion rates. 3. Customer Segmentation: AI and ML algorithms can analyse customer data to identify distinct segments based on various characteristics such as demographics, interests, and behaviours. This segmentation helps marketers understand different customer groups and develop targeted marketing campaigns tailored to each segment’s preferences and needs. AI and ML algorithms can be applied to customer segmentation in digital marketing strategies to identify distinct customer groups based on various characteristics. One commonly used algorithm for customer segmentation is the clustering algorithm. Here’s an overview of how AI and ML algorithms can
3 Proven Strategies to Boost Your AI ROI | Baking AI
Are you investing in AI but struggling to see a real payback? You’re not alone. Many companies get caught in the hype cycle but fail to translate AI into tangible business benefits. But what if you could unlock AI’s true profit potential? Artificial intelligence (AI) has the potential to revolutionize businesses, but many companies are struggling to see a return on investment (ROI) from their AI projects. This article will discuss the challenges of measuring ROI from AI and outline three key strategies that can help businesses improve their AI ROI. The Challenges of Measuring ROI from AI Measuring the return on investment (ROI) for AI initiatives is crucial for organizations to assess the effectiveness of their investments. Let’s explore some strategies and key considerations: Hard ROI vs. Soft ROI: Hard ROI: It focuses on direct financial gains or cost savings from AI implementation. Examples include time savings, productivity increases, and reduced operational costs. Soft ROI: A broader set of benefits, including employee satisfaction, brand enhancement, and higher company valuation. Soft ROI considers factors beyond immediate financial gains. Sources of Hard ROI in AI: Time Savings: AI automates repetitive tasks, reducing processing time (e.g., invoice processing). Productivity Increase: Assisted intelligence enhances decision-making and improves employee productivity (e.g., anti-money laundering compliance). Cost Savings: Reduced labor costs due to time and productivity gains (e.g., reduced data entry operators). Accounting for Time Value and Uncertainty: ROI calculations should consider both the time value of money invested and the uncertainty of benefits. Benefits may accrue at an unspecified future point, so account for timing uncertainties. Control Costs: Use AI services InData Labs, 247.ai, Baking AI, and Data Forest to reduce expenses related to third-party products. Streamline workflows to save time and effort, leading to cost savings. Prioritize Explainability and Trust: Model interpretability ensures transparency and trust. Incorporate ethical considerations to avoid bias and ensure fairness. 3 Strategies for Improving AI ROI Despite the challenges, there are several things that businesses can do to improve their AI ROI. Here are three key strategies: Track ROI from the beginning What it means: Start measuring the impact of your AI project right from the start. Why it matters: By tracking ROI early, you can identify what’s working well and where improvements are needed. How to do it: Define clear goals: Understand what success looks like for your AI project. Measure outcomes: Quantify the benefits (e.g., time saved, cost reduction) as the project progresses. Adjust as needed: Use the data to make informed decisions and optimize your approach. Control Costs: What it means: Be mindful of how much you’re spending on AI initiatives. Why it matters: Overspending can eat into your ROI. How to do it: Scope projects carefully: Focus on high-impact areas and avoid unnecessary complexity. Use cost-effective tools: Consider cloud-based platforms that offer scalability without hefty upfront costs. Monitor expenses: Regularly review and adjust your budget as needed. Focus on Mundane Tasks: What it means: Apply AI to repetitive, time-consuming tasks. Why it matters: Automating these tasks frees human resources for more strategic work. How to do it: Identify bottlenecks: Look for tasks that take up much time but don’t require complex decision-making. Automate with AI: Use machine learning or robotic process automation to handle these tasks efficiently. Empower employees: Allow them to focus on creative, value-added work. Remember, improving AI ROI involves a mix of smart tracking, cost control, and strategic focus. By implementing these strategies, you’ll be on your way to maximizing the value of your AI investments! How do I know if my AI project is successful? Organizations should properly establish KPIs to measure the success of AI projects, improve their efficiency, and enable them to improve society. Classic AI implementation, for starters, involves machine learning to establish basic models and algorithms and then architect training methods. After the training process, developers measure their training data against their predicted results to make modifications and reduce errors over time. There are different types of models, but in all models, it is important to have a measurable output against a known input for training to be effective. Choosing the right tools for the job is equally important, with elements that replicate real-world scenarios as closely as possible. Some key ways to determine if your AI project is successful: Meeting Defined Goals: Clearly defined objectives: Success hinges on having well-defined goals at the project’s outset. These goals should be SMART — Specific, Measurable, Achievable, Relevant, and Time-bound. Did your AI solution address the problem you set out to solve? Metrics and Benchmarks: Establish quantifiable metrics relevant to your goals. Track these metrics throughout the project and compare them to pre-established benchmarks. Did your AI project achieve the targeted improvements in efficiency, accuracy, cost reduction, or other relevant factors? Real-World Impact and ROI: Tangible Business Value: Beyond technical success, did your AI project positively impact the business? Did it increase revenue and cost savings, improve customer satisfaction, or enhance decision-making? Return on Investment (ROI): This metric is crucial for justifying future AI investments. Track costs associated with the project (development, data, infrastructure) and compare them to the measurable benefits it generates. Is the project generating a positive ROI compared to the initial investment? User Satisfaction and Adoption: User Feedback: If your AI solution interacts with users, gather their feedback through surveys, interviews, or focus groups. Are users finding the AI helpful, easy to use, and reliable? User Adoption: Is your AI solution actively used by its intended audience? High user adoption indicates that the AI is valuable and provides a positive user experience. Long-Term Sustainability: Scalability and Adaptability: Consider if your AI solution can be scaled to handle increased demand or adapted to changing needs. A successful project should be designed for long-term use and value generation. Maintainability and Explainability: Can your AI project be easily maintained and updated as needed? Is the AI’s decision-making process transparent enough to be understood and trusted by users? A successful project prioritizes ongoing maintenance and interpretability. Remember, success isn’t always a binary outcome. Even if some goals aren’t fully met, the learnings gained can inform future AI
Introducing OpenAI’s Sora: AI-Powered Video Creation Tool
Have you heard of OpenAI? They created a new AI tool called Sora. It’s a super fancy movie maker that listens to your ideas and turns them into short videos! Imagine describing a scene with a fantastic character and a neat background, and Sora brings it to life! This could be helpful for all sorts of things, like making cartoons or explaining ideas with videos. It’s still under development, but it has the potential to be amazing! What is OpenAI’s Sora? OpenAI’s Sora is a cutting-edge AI tool that lets you create videos simply by describing them in text. Imagine you have an idea for an extraordinary animation or a short explainer video but lack the filming or animation skills to bring it to life. Sora can help! You provide a written description of the scene, characters, and actions you want, and Sora uses its artificial intelligence to generate a short video based on your instructions. It’s like having a superpowered movie maker in your pocket, ready to turn your ideas into visual stories. This technology can potentially revolutionize how we create content across various fields, from marketing and education to entertainment and personal projects. How Does Sora Work? Sora uses a fascinating combination of artificial intelligence techniques to translate your text description into a video. Here’s a simplified breakdown of the process: Text Prompt Input: You provide a clear and detailed description of the video you envision. This could include information about the setting, characters, actions, and overall mood. The more specific your description, the better Sora can understand your vision. AI Processing: Sora’s internal AI engine goes to work! It analyzes your text prompt and uses its knowledge of the natural world, object interactions, and motion to translate it into a sequence of images. Video Generation: Imagine a blurry picture slowly coming into focus. That’s what happens here! Sora progressively refines the generated images, adding details, textures, and movement until you have a short video clip based on your description. Current Limitations (Transparency is Key): It’s important to remember that Sora is still under development. While it can create some awe-inspiring visuals, there might be some limitations in the current version: Video Length: Currently, Sora might only generate videos up to a certain length (e.g., 30 seconds, 1 minute). Complexity: Very intricate scenes with complex movements or special effects might be challenging for Sora at this stage. Fine-Tuning: Matching your exact vision ideally might require some trial and error with your text prompts. However, the world of AI is constantly evolving, and these limitations will likely improve over time. By understanding these potential limitations, you can set realistic expectations and get the most out of using Sora! Use Cases for Sora Sora’s potential to transform content creation across various industries is fascinating. Here’s a glimpse into how Sora can be a game-changer: Content Creation Revolution Effortless Explainer Videos: Imagine explaining complex topics like financial services or scientific concepts. With Sora, you can create engaging explainer videos in minutes simply by describing the content and visuals. Boosting Marketing & Social Media: Need eye-catching product demos or social media content? Sora can help you generate product presentations or short, creative video snippets to grab your audience’s attention. Boosting Education Bringing Textbooks to Life: Imagine historical events or scientific phenomena jumping off the textbook page! Sora can transform static content into dynamic videos, making learning more engaging and interactive for students. Visualizing Complex Concepts: Struggling to explain a challenging concept? Use Sora to create clear and concise video representations, helping students grasp even the most intricate topics. Entertainment on Demand Storyboarding & Animation Made Easy: Storytellers and aspiring animators rejoice! Sora can help you visualize your ideas by turning storyboards into short animated clips. Personalized Video Greetings: Want to add a special touch to birthdays or holidays? Let Sora generate unique video greetings with custom messages, making celebrations more memorable. The Future of Storytelling: What’s Next for Sora? Sora’s emergence marks a significant leap in AI-powered video creation, but it’s just the beginning. Here’s what the future might hold for this groundbreaking technology: Exciting Advancements: Longer Videos: Imagine creating entire short films or detailed explainer videos using Sora! As the technology progresses, Sora will likely be able to generate more extended and complex video content. Real-Time Video Generation: The dream of describing a scene and instantly seeing it come to life on screen might become a reality. Future advancements could allow Sora to generate videos in real-time, opening doors for interactive experiences and live presentations. Ethical Considerations and Challenges: While Sora offers incredible potential, there are important ethical questions to consider: Misinformation and Deepfakes: The ability to create realistic videos with AI raises concerns about spreading misinformation and deepfakes. Open and transparent discussions are crucial to ensure the responsible use of this technology. Artistic Ownership and Creativity: As AI plays a more significant role in content creation, questions about ownership and creative credit arise. Finding the right balance between human and AI contributions is essential. By acknowledging these challenges and working towards solutions, we can ensure that Sora and similar AI tools are used ethically and responsibly, unlocking their full potential for positive creative expression. Getting Started with Sora While Sora isn’t publicly available, there’s still plenty to explore! Here’s what we know and some tips to prepare for its arrival: Access and Release: OpenAI is currently focused on gathering feedback and ensuring responsible development. They’ve offered access to a limited group for testing and haven’t announced a specific public release date. Stay tuned for updates! In the meantime, you can explore OpenAI’s website and follow their social media channels for the latest news on Sora’s development. Crafting Effective Text Prompts: Even when Sora does become available, getting the most out of it will depend on how you communicate your ideas. Here are some tips for crafting effective text prompts: Clarity is Key: Be as clear and concise as possible in your descriptions. The more specific you are about the setting, characters, actions, and desired tone, the better Sora can understand your vision. Details Matter: Don’t skimp on details!
LLM in Recommendation Systems: Challenges & Future Prospects
Imagine recommendation systems that ditch the one-size-fits-all approach. Instead, picture conversing with a super-smart friend who knows your tastes and can suggest things you’ll enjoy. This is the future with Large Language Models (LLMs) in recommendation systems! LLMs are like super-powered language tools. Trained on massive amounts of text, they can understand the nuances of human conversation. This lets them analyze your past choices, reviews you’ve written, and even comments you’ve left — like getting to know you through a chat. LLMs can use this knowledge to recommend things that fit your specific interests and mood. Motivation The explosion of recommendation systems happened as online services grew, helping users handle information overload and find better quality content. These systems aim to figure out what users like and suggest items they might enjoy. Deep learning-based recommendation systems focus on this by ranking items for users, whether it’s offering top picks or recommending things in a sequence. On the other hand, large language models (LLMs) are super bright in understanding language, doing things like reasoning and learning from just a few examples. They also have loads of knowledge packed into their systems. So, the question is: How can we use these LLMs to make recommendation systems even better? Let’s break down the blog into four parts: Where to Use LLMs: We’ll explore where it’s wise to bring them into recommendation systems. Sometimes, more straightforward solutions work better, so we’ll find the right balance. Integrating LLMs: Here, we’ll talk about how to mix LLMs into existing recommendation setups. You can adjust LLMs to your data or keep them as is. Plus, we’ll discuss combining traditional recommendation methods with LLMs. Challenges: We’ll tackle the hurdles that come up in the industry when using these new methods. Future Improvements: Finally, we’ll discuss how to make LLMs even more helpful in recommendation systems. Where to adapt LLMs? Large Language Models (LLMs), like the ones we’ve been discussing, have a lot of potential. But where can we use them? Imagine a pipeline that helps recommend things to users, like products or content. Here’s how it works: User Data Collection: We gather information from users. This can be explicit (like ratings) or implicit (like clicks on a website). Feature Engineering: We take the raw data we collected and turn it into something structured. Think of it like organizing a messy room into neat boxes. Feature Encoding: We create special codes (called embeddings) for the features. It’s like translating the data into a language the computer understands. Scoring/Ranking: We use machine learning to determine which items are most relevant to users. It’s like picking the best movie to watch from a long list. Pipeline Control: We manage the whole process. Imagine a traffic controller making sure everything runs smoothly. Now, where do LLMs fit in? Well, they can help at different stages: Understanding User Input: LLMs can read and understand what users say, even if they type it naturally. Generating Better Features: LLMs can help create better features from messy data. Improving Scoring/Ranking: LLMs can learn from patterns and suggest better ways to rank items. Fine-Tuning the Pipeline: LLMs can help fine-tune the process for better results. So, LLMs are like helpful assistants in this recommendation pipeline! LLM for feature engineering What LLM can do here is take the original data input and generate additional textual features as a way to augment the data. This approach was demonstrated to work well with tabular data and was further extended by using the LLMs to align out-of-domain datasets on the shared task. They can also be used to generate tags and model user interest. LLM as feature encoder For conventional recommendation systems, the data is usually transformed into one-hot encoding + an embedding layer is added to adopt dense embeddings. With LLMs, we can improve the feature encoding process by adding better representations for downstream models via the semantic information extracted and applied to the embeddings and achieving better cross-domain recommendations where feature pools might not be shared. For example, UNBERT uses BERT to improve news recommendations via better feature encoding. ZESREC applies BERT to convert item descriptions into a zero-shot representation of a continuous form. LLM for scoring/ranking A common approach explored is using the LLM to rank the items based on relevance. Many methods use a pipeline where the output of LLM is fed into a projection layer to calculate the score on the regression or classification task. However, recently, some researchers proposed using the LLM instead to deliver the score directly. TALLRec, for example, uses LLM as a decoder to answer a binary question appended to the prompt; another team used LLM to predict a score textually and formatted it with careful prompting. LLMs can also be successfully used for direct item generation. This would be similar to our approach in the previous blog post. These approaches also can be hybridized and used in tandem. LLM as a pipeline controller This approach largely stems from the notion that LLMs possess emergent properties, that is, they can perform tasks that smaller models could not; these can be in context learning and logical reasoning. However, you should be aware that many researchers actively dispute the claims that LLMs possess any emergent abilities and imply that these are instead a product of imperfect statistical methods that bias the evaluation, suggesting it may not be a fundamental property of scaling AI models. Some researchers even suggested a complete framework that utilizes the LLM to manage the dialogue, understand user preferences, arrange the ranking stage, and simulate user interaction. You may have noticed that the user data collection piece needs to be included. Not much work has been done to explore the LLM’s potential in this domain. LLMs here can filter biased, hateful data and select the best representations or find meaningful, relevant information from a sea of input. They can be used in surveys as customer experience collectors and many more. How to adapt LLMs? Now that we know where to include the models in our pipeline, let’s talk about how we can do so. Generally, we
Stop AI Cheating! WARM Makes AI More Reliable
Have you ever wondered if your friendly AI assistant is secretly manipulating you? It’s not science fiction anymore. Large language models (LLMs) can exploit weaknesses in reward systems, leading to “reward hacking.” But fear not, a new technique called Weight-Averaged Reward Models (WARM) is here to save the day! Image Credit: MarkTechPost Can AI cheat its way to success? Imagine training a language model to write summaries of news articles. You reward it for producing summaries that are accurate and informative. However, the model might find ways to “game the system” by focusing on keywords that earn high rewards, even if the summaries are nonsensical or misleading. This is called “reward hacking.” Here’s where Weight-Averaged Reward Models (WARM) come in. Instead of relying on a single reward system, WARM creates several different ones, each with its strengths and weaknesses. Then, it combines these “perspectives” into a single, more robust system, making it harder for the AI to exploit loopholes. Think of it like having a group of experts judge the summaries. Each expert has their criteria, but by combining their evaluations, you get a more accurate and holistic assessment than relying on any single opinion. This approach helps build more reliable AI systems by aligning their goals with what we truly care about. It’s like training a good student who understands and strives to achieve the actual purpose, not just “pass the test” by any means. Exploring Weight-Averaged Reward Models (WARM) Imagine training a robot to clean your room. In traditional methods, the robot learns by trial and error, getting “rewards” for good behavior (like picking up toys) and figuring out what works best. However, this can be slow and unreliable, especially in complex situations like a messy room with many different objects. Here’s where Weight-Averaged Reward Models (WARM) come in. WARM is a new approach that helps robots learn faster and more reliably by: Remembering past successes: Instead of just focusing on the current reward, WARM considers past “good” choices the robot made. This helps it avoid repeating mistakes and learn from its experiences. Smoothing out the learning process: Learning can be bumpy, with the robot sometimes making mistakes even when trying its best. WARM smooths out these bumps by averaging the rewards over time, making the learning process more stable and efficient. With WARM, the robot becomes a quicker learner, able to adapt to different situations and clean your room more efficiently, just like a pro! Training Deep Reinforcement Learning Models: The Challenges Training Deep Reinforcement Learning (RL) models isn’t a walk in the park. These models learn through interacting with their environment, which can be quite difficult for several reasons: Need for Lots of Practice: Unlike humans who might learn quickly, deep RL models often need a lot of experience (think interactions with the environment) to learn good policies (rules for making decisions). This can be a problem in complex situations where each interaction might be expensive or time-consuming. Balancing Exploration and Exploitation: Imagine an RL model trying to learn a game. It needs to balance exploration (trying new things) with exploitation (using what it already knows to get rewards). If it only explores, it might never learn good strategies. If it only exploits, it might miss out on better options. Finding the right balance is crucial. Dealing with Complex Environments: The real world is messy! Deep RL models need to handle situations with many different possibilities and choices (think high-dimensional spaces). This can be overwhelming for traditional RL algorithms, making it difficult for them to learn effectively. Getting Stuck or Unstable: Sometimes, deep RL models can get stuck during training, or they might learn something that isn’t the best. This is called convergence and stability issues, and it’s important to ensure models learn effectively and don’t get stuck in bad situations. Adapting to Change: What if the rules of the game suddenly change? Deep RL models need to be able to adapt to non-stationary environments where things aren’t always the same. Additionally, they might not always have complete information about their surroundings (think partial observability). These factors make learning even more challenging. Giving Credit Where Credit is Due: Imagine playing a long game and getting a reward at the end. How do you know which of your earlier actions led to the reward? This is the credit assignment problem, and deep RL models must learn from their experiences and attribute success or failure to the correct actions. Learning from Past Experiences: Deep RL models often struggle to apply what they’ve learned to new situations. Transfer learning aims to use knowledge from previous tasks to speed up learning on new ones, but it’s still a challenge to achieve good transferability. These are just some of the challenges facing deep RL models. Researchers are constantly working on new techniques to overcome these limitations and make these models more efficient and effective in real-world applications. Reinforcement Learning Experiments Imagine a robot trying to navigate a maze. It learns by trying different paths, receiving rewards for reaching the goal, and penalties for hitting walls. This is the essence of reinforcement learning (RL) experiments. Think of these experiments as training grounds for AI agents. In controlled environments, the agents interact with their surroundings, like a maze or a game. They learn from their experiences and adjust their actions to maximize their rewards. For example, the robot learns to avoid walls to get the highest reward for reaching the goal. These experiments are crucial for understanding how RL algorithms work, how effective they are, and how we can improve them. They help us see how the agents learn, adapt, and make decisions in different situations. Imagine having a group of friends who are all good at different things. One is great at math, another is a history whiz, and another is a master of current events. By working together, they can tackle any question you throw their way! Ensembling in machine learning works similarly. It combines the predictions of several individual models
Open Source LLMs: Open Source vs. Proprietary Large Language Models
Language Models (LMs) have revolutionized natural language processing (NLP) tasks by achieving state-of-the-art performance in various domains. Often based on deep learning architectures, these models learn to predict the next word in a sequence given the context. Recently, large-scale LMs, such as GPT-3 and BERT, have gained prominence due to their impressive capabilities. Open Source vs. Proprietary LLMs Open Source LLMs are like friendly, open books. Their source code, model architecture, and pre-trained weights are publicly available. You can peek inside, see how they work, and even customize them. Plus, they’re free! Anyone can use, modify, and distribute them. Imagine a community garden where everyone shares seeds and gardening tips. On the other hand, Proprietary LLMs are like secret recipes. Their source code and weights are locked away. You can’t tweak them much; they’re like a fixed menu at a fancy restaurant. But they might perform better and be more secure. However, you pay for access—think of it as dining at an exclusive restaurant. Picture a chef guarding their secret sauce recipe. So, which to choose? Open source is excellent for budget-friendly, adaptable solutions, while proprietary models are sometimes better-performing but pricier. It’s like choosing between a community potluck and a gourmet meal—both have their place! Open Source LLMs Advantages: Community Collaboration: Open-source LLMs encourage collaboration among researchers, developers, and practitioners. The community contributes to model improvements, bug fixes, and fine-tuning. Transparency: Open source models allow users to inspect the architecture, weights, and training data. Transparency is crucial for understanding biases and potential ethical concerns. Cost-Effectiveness: Access to open-source models is free, making them attractive for startups, researchers, and hobbyists. Challenges: Resource Intensive: Training large LLMs requires significant computational resources (GPUs, TPUs, etc.). Smaller organizations may need help with this. Fine-Tuning Complexity: While pre-trained models are available, fine-tuning for specific tasks can be complex and time-consuming. Quality Control: Open source models vary in quality, and not all are suitable for production use. 2. Proprietary LLMs Advantages: Vendor Support: Proprietary LLMs provide vendor support, including documentation, updates, and troubleshooting. Ease of Use: Some proprietary models offer user-friendly APIs, simplifying integration. Customization: Vendors may allow fine-tuning on proprietary models, tailoring them to specific tasks. Challenges: Cost: Proprietary models often come with licensing fees, which can be prohibitive for small businesses. Black Box: Proprietary models lack transparency. Users cannot inspect the inner workings or biases. Vendor Lock-In: Relying solely on proprietary models ties you to a specific vendor. Considerations for Choosing an LLM Task Requirements: Consider the specific NLP task (e.g., sentiment analysis, text generation, question answering). Evaluate whether an existing open-source model meets your needs or if fine-tuning is necessary. Ethical and Bias Concerns: Investigate biases present in pre-trained models. Open-source models allow bias mitigation through fine-tuning. Resource Availability: Assess your organization’s computational resources. Proprietary models may offer cloud-based solutions. Cost-Benefit Analysis: Weigh the benefits of transparency and community collaboration against the cost of proprietary models. List of Most Popular Open Source LLMs of 2024 Some popular open-source Large Language Models (LLMs) have gained traction in the field of natural language processing. These models are freely available for use and offer exciting possibilities: Llama 2: Llama 2 is a modern set of really advanced text models. They come in different sizes, from smaller ones with 7 billion parameters to huge ones with 70 billion parameters. These models are super fancy and are used for all sorts of things. One particular type of Llama 2 model is called Llama-2-Chat. These are tweaked to be good at having conversations. They’ve been worked on a lot to make sure they’re better than other chat models you can find for free. People have checked them out and think they’re great at being helpful and keeping things safe. They’re just as good as other popular models you might have heard of, like ChatGPT and PaLM. Here are the details of this model: Parameters: 7B, 13B, and 70B License: Custom commercial license available at Meta’s website. Release Date: July 18, 2023 Paper: “Llama-2: Open Foundation and Fine-tuned Chat Models” HuggingFace: https://huggingface.co/meta-llama/Llama-2-7b Training Database: Llama 2 was pre-trained on 2 trillion tokens from public data, then fine-tuned with over a million human-annotated instances and public instruction datasets. Meta claims that no metauser data was used in either phase. Variants: Llama 2 is available in multiple parameter sizes, including 7B, 13B, and 70B. Both pre-trained and fine-tuned variations are available. Fine-tuning Techniques: The model employs supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to better align with human preferences, ensuring helpfulness and safety. OpenLLaMA: OpenLLaMA is a freely available model inspired by Meta AI’s popular LLaMA model. It’s open-source, meaning anyone can use it. OpenLLaMA comes in various sizes, ranging from 7 billion to 65 billion parameters, and it’s trained on a whopping 200 billion tokens of data. Here are the details of OpenLLaMA: Parameters: 3B, 7B and 13B License: Apache 2.0 Release Date: May 5, 2023 Github: https://github.com/openlm-research/open_llama Paper: Meet OpenLLaMA: An Open-Source Reproduction of Meta AI’s LLaMA Large Language Model HuggingFace: OpenLLaMA: An Open Reproduction of LLaMA Training Database: OpenLLaMA was trained using the RedPajama dataset, which has over 1.2 trillion tokens. The developers followed the same preprocessing and training hyperparameters as the original LLaMA paper. Fine-tuning Techniques: The OpenLLaMA has the same model architecture, context length, training steps, learning rate schedule, and optimizer as the original LLaMA paper. The main difference between OpenLLaMA and the original LLaMA is the dataset used for training. Falcon: The Technology Innovation Institute in Abu Dhabi created the Falcon models. They’re top-notch and cutting-edge language models known as the Falcon family. Among them, the Falcon-40B stands out as particularly impressive. It’s so good that it can go head-to-head with several other advanced language models that aren’t available to the public. Here are the details of the Falcon model: Parameters: 7B and 40B License: Apache 2.0 Release Date: June 5, 2023 Paper: The Falcon has landed in the Hugging Face ecosystem HuggingFace: https://huggingface.co/tiiuae/falcon-7b Variants: Falcon-40B: A heavyweight in the Falcon family, model is powerful and efficient, outperforming the LLaMA-65B with 90GB of GPU memory. Falcon-7B: Falcon-7B is a top-performing, smaller version that only needs 15GB for consumer hardware. Training Database: The Falcon-7B and Falcon-40B
HNSW : Semantic Search Using FAISS
Introduction In various data-driven fields such as data science, similarity search frequently arises within the realm of natural language processing (NLP), search engines, and recommender systems, tasked with identifying the most pertinent documents or items related to a given query. Enhancing search efficiency in vast datasets encompasses a diverse array of methodologies. Hierarchical Navigable Small World (HNSW) stands out as a cutting-edge algorithm employed for approximating nearest neighbour searches. Leveraging optimized graph structures, HNSW operates distinctively from conventional methods, rendering it a formidable tool in the realm of similarity search. Before delving into the intricacies of HNSW, it’s essential to explore skip lists and navigable small worlds, pivotal data structures employed within the HNSW framework. Skip lists Skip list, a probabilistic data structure, facilitates efficient insertion and search operations within a sorted list, boasting an average complexity of O(logn). This structure comprises multiple layers of linked lists. At its base lies the original linked list containing all elements. As one ascends through the layers, the number of elements skipped grows, resulting in fewer connections and expedited traversal. The search procedure for a particular value commences at the highest level, where it evaluates the next element against the sought-after value. If the value is either less than or equal to the element, the algorithm advances to the subsequent element. Conversely, if the value surpasses the element, the search procedure transitions to a lower layer characterized by more extensive connections, repeating the comparison process. Ultimately, the algorithm descends to the lowest layer to pinpoint the desired node. Drawing from insights on Wikipedia, skip lists introduce a pivotal parameter denoted as p, which determines the likelihood of an element appearing in multiple lists. If an element is present in layer i, the probability of its presence in layer i + 1 equals p (typically set to 0.5 or 0.25). On average, each element manifests in approximately 1 / (1 — p) lists. Evidently, this approach outpaces the conventional linear search method employed in linked lists. Notably, HNSW adopts a similar concept, albeit utilizing graphs instead of linked lists. Navigable Small World Navigable Small World graph is characterized by its polylogarithmic search complexity, denoted as T = O(logᵏn), wherein the process of greedy routing is employed. Routing in this context involves initiating the search from vertices with low degrees and progressing towards those with higher degrees. This strategic approach leverages the sparse connections of low-degree vertices to swiftly traverse between them, facilitating efficient navigation towards the area where the nearest neighbor is anticipated to reside. Subsequently, the algorithm seamlessly transitions to high-degree vertices, gradually narrowing down the search space to pinpoint the nearest neighbor within that specific region. Vertex is sometimes also referred to as a node. Search Initially, the search process commences with the selection of an entry point. This pivotal step sets the stage for subsequent vertex determinations within the algorithm. The next vertex, or vertices, are determined based on the calculation of distances from the query vector to the neighboring vertices of the current vertex. Subsequently, the algorithm progresses by moving towards the closest vertex. Eventually, the search reaches its conclusion when there are no neighboring nodes closer to the query than the current node, which is then returned as the query’s response. Although employing a greedy strategy, this approach lacks a guarantee of finding the precise nearest neighbor, relying solely on local information at each step to dictate its decisions. Notably, a common issue encountered is early stopping, particularly noticeable at the outset of the search process, where superior neighboring nodes are absent. This predicament is exacerbated in scenarios with an abundance of low-degree vertices within the starting region. To enhance search accuracy, employing multiple entry points proves advantageous. Construction The construction process of the NSW (Neighborhood Subgraph Walk) graph involves iteratively incorporating dataset points into the existing graph through shuffling and sequential insertion. Upon insertion of a new node, it establishes connections with the M nearest vertices, fostering the formation of edges that facilitate efficient graph navigation. Typically, during the initial stages of graph construction, longer-range edges tend to emerge, serving as pivotal conduits for traversing the graph. These edges assume significance in enabling seamless navigation throughout the network. Illustrated in the accompanying figure is the significance of an early-introduced long-range edge, exemplified by AB. This edge, forged in the initial stages, proves invaluable for scenarios requiring traversal between distant nodes such as A and I. By bridging disparate regions of the graph, the AB edge facilitates swift navigation, enhancing query efficiency. Furthermore, as the graph expands with the addition of more vertices, there arises an augmented likelihood of shorter lengths for newly established edges connecting to a freshly incorporated node. This phenomenon contributes to the continual refinement and densification of the graph structure, fostering enhanced connectivity and traversal efficiency. HNSW HNSW, or Hierarchical Navigable Small World, operates on foundational principles akin to those found in skip lists and navigable small worlds. This innovative approach entails a multi-layered graph structure, where the upper layers exhibit sparser connections while the lower layers comprise denser regions. This hierarchical arrangement enables efficient navigation and proximity searches within the data space, facilitating quicker retrieval of relevant information. Search The search process begins at the uppermost layer and systematically descends, iteratively identifying the nearest neighbor within each layer’s node set. This methodically progresses until the closest neighbor within the lowest layer is determined, serving as the final answer to the query at hand. In a manner akin to NSW, the efficacy of HNSW’s search can be heightened through the utilization of multiple entry points. Rather than solely identifying a single nearest neighbor within each layer, the process of efSearch, governed by a hyperparameter, locates a specified number of closest nearest neighbors to the query vector. Subsequently, each of these neighbors serves as an entry point for the subsequent layer, enhancing the overall search quality. Complexity The original paper’s authors assert that the computational effort needed to locate the nearest neighbor at any
Retrieval-Augmented Generation (RAG) : Keeping LLMs Custom
In the wake of recognizing the potential to augment Large Language Models (LLMs) with proprietary data, considerable discourse has arisen regarding the optimal approach for effectively bridging the gap between the LLM’s general knowledge and proprietary datasets. A pivotal point of contention revolves around the comparison of fine-tuning and Retrieval-Augmented Generation (RAG) techniques, with the unequivocal conclusion being that a combination of both methodologies yields optimal results. This article meticulously delves into the intricacies of the Retrieval-Augmented Generation (RAG) framework, commencing with an in-depth exploration of its theoretical underpinnings. Subsequently, it expounds upon the practical implementation of a RAG pipeline, elucidating the orchestration process employing LangChain, the integration of OpenAI language models, and the utilization of a Weaviate vector database to enhance retrieval mechanisms. What is Retrieval-Augmented Generation? Retrieval-augmented generation (RAG) represents an advanced paradigm aimed at endowing Language Models (LLMs) with augmented information retrieved from external knowledge sources. By integrating this supplementary data, LLMs can proficiently generate responses that exhibit enhanced accuracy and contextual relevance, thereby mitigating instances of hallucinatory outputs. Issue Cutting-edge Large Language Models (LLMs) leverage extensive datasets for training, aiming to encompass a diverse range of general knowledge encoded within the neural network’s parametric memory. Nonetheless, soliciting an LLM to produce a completion necessitating information beyond its training data—such as recent, proprietary, or domain-specific data—may result in factual inaccuracies, commonly referred to as hallucinations. The phenomenon is exemplified in the accompanying screenshot : It is imperative to mitigate the disparity between the Language Model’s (LLM) inherent general knowledge and supplementary contextual information. This proactive approach serves to optimize the LLM’s capacity for generating precise and contextually nuanced completions, concurrently minimizing the occurrence of hallucinatory outputs. Resolution Historically, the customization of neural networks for domain-specific or proprietary data involved the meticulous fine-tuning of models. While this approach has demonstrated efficacy, it is characterized by computationally intensive processes, significant cost implications, and a prerequisite for substantial technical expertise, thereby diminishing its adaptability to rapidly evolving information landscapes. In 2020, Lewis et al. introduced a paradigm-shifting methodology known as Retrieval-Augmented Generation (RAG), as elucidated in their paperwork titled “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks“. This innovative framework amalgamates a generative model with a retriever module, enabling the incorporation of supplementary information sourced from external knowledge repositories. Notably, this approach facilitates seamless updates to the external knowledge source, enhancing the adaptability and responsiveness of the overall system. Relational Access Guidance (RAG) Framework In succinct terms, the Relational Access Guidance (RAG) framework can be analogously likened to the relationship between Language Models (LLMs) and an open-book examination within the domain of artificial intelligence. Much like the allowance of reference materials in an open-book exam facilitates the retrieval of pertinent information for human response, RAG segregates factual knowledge from LLMs’ reasoning faculties. This is achieved through the maintenance of an externalized knowledge repository, ensuring seamless accessibility and dynamic updates. The bifurcation of knowledge in the RAG framework consists of two distinct categories: Parametric Knowledge: Acquired implicitly during the model’s training phase and intricately encoded within the neural network’s weights. Non-parametric Knowledge: Deliberately extricated and preserved in an external knowledge repository, exemplified by a vector database. This segregation echoes the objective of emphasizing the LLM’s cognitive reasoning capacities over the rote memorization of specific details. It is pertinent to acknowledge that JJ initially posited the ingenious analogy between RAG and an open-book exam during the Kaggle — LLM Science Exam competition. Retrieve: The user query serves as the catalyst for extracting pertinent context from an external knowledge source. In this process, the user query undergoes embedding using a sophisticated model, aligning it within the same vector space as the supplementary context stored in the vector database. This alignment facilitates a nuanced similarity search, ultimately yielding the retrieval of the top k closest data objects from the vector database. Augment: The user query and the procured additional context seamlessly integrate into a meticulously designed prompt template. Generate: Subsequently, the retrieval-augmented prompt is seamlessly channeled into the Language Model (LLM) for the final generation phase. Retrieval-Augmented Generation Implementation using LangChain This segment deploys a RAG (Retrieval-Augmented Generation) pipeline in Python, harnessing the power of an OpenAI Language Model (LLM) in tandem with a Weaviate vector database and an OpenAI embedding model. The orchestration is facilitated through the utilization of LangChain. Prerequisites Ensure that you have installed the essential Python packages for optimal functionality: Langchain – utilized for orchestration purposes. Openai – indispensable for the embedding model and Large Language Model (LLM) integration. Weaviate-client – necessary for seamless interaction with the vector database. !pip install langchain openai weaviate-client Furthermore, define your relevant environment variables in a .env file in your root directory. To procure an OpenAI API Key, it is imperative to possess an active OpenAI account. Subsequently, navigate to the API keys section and initiate the process by selecting the option to “Create a new secret key.” OPENAI_API_KEY=“<YOUR_OPENAI_API_KEY>” Then, run the following command to load the relevant environment variables. import dotenv dotenv.load_dotenv() Preparation In the initial phase of preparation, it is imperative to assemble a comprehensive vector database serving as an external knowledge repository containing pertinent supplementary information. The construction of this vector database involves a systematic sequence of actions: Acquire and Load Data Document Chunking Embedding and Storage of Chunks The initial step involves collecting and loading your data. In this instance, we will utilize Langchain documentation to provide additional context. To facilitate data loading, one can employ any of LangChain’s numerous built-in DocumentLoaders. A Document, in this context, refers to a dictionary containing both text and metadata. For loading the text specifically, LangChain’s WebBaseLoader will be employed. import requests from langchain.document_loaders import WebBaseLoader url = “https://python.langchain.com/docs/get_started/introduction” loader = WebBaseLoader(url) documents = loader.load() Next, segment your documents—The original document is too lengthy to seamlessly fit into the context window of the LLM. Therefore, it is necessary to divide it into smaller sections. LangChain provides several built-in text splitters designed for this purpose. In this straightforward example, the CharacterTextSplitter can be employed, setting the chunk
Utilizing Pre-trained Large Language Models (LLMs) in Recommender Systems
Recently, recommendation systems have improved to provide more detailed and personalized suggestions using large language models (LLM). However, combining LLMs’ general knowledge and reasoning skills into these systems is still difficult. This paper introduces a new model called RecSysLLM, a recommendation model that uses LLMs. RecSysLLM keeps the LLM’s ability to reason and its knowledge and adds knowledge specific to recommendations. This is done through special methods of handling data, training the model, and making inferences. As a result, RecSysLLM can use LLMs’ abilities to recommend items in a single, efficient system. Tests on standard measures and real-world situations show that RecSysLLM works well. RecSysLLM is a hopeful method for creating recommendation systems that fully use pre-trained language models. Enhancing Recommender Systems with Large Language Models Recommender Systems (RSs) are important tools for personalized suggestions in many areas, like online shopping and streaming services. New developments in language processing have led to Large Language Models (LLMs), which are very good at understanding and creating text that sounds like a person wrote it. RSs are known for being very good and efficient in well-defined areas, but they aren’t adaptable and can’t give suggestions for data they haven’t seen before. On the other hand, LLMs are aware of the context and can adapt well to data they haven’t seen. Combining these technologies gives you a powerful tool to give relevant suggestions that fit the context, even when there isn’t much data. This proposal looks into how LLMs can be added to RSS. It introduces a new method called Retrieval-augmented Recommender Systems, which uses the best parts of models based on retrieval and generation to improve how well RSs can give relevant suggestions. Enhancing LLMs to construct personalized reasoning graphs Recommendation systems are designed to give users suggestions that are relevant to them. However, these systems often struggle to explain their decisions and understand the deeper connections between a user’s actions and profile. This paper introduces a new method that uses large language models (LLMs) to create personalized reasoning graphs. These graphs connect a user’s profile and behavior using cause-and-effect and logical connections, making it easier to understand their interests. Our method, called LLM reasoning graphs (LLMRG), has four parts: Chained graph reasoning Divergent extension Self-verification and scoring Improving the knowledge base The created reasoning graph is then turned into code using graph neural networks. This code is used as extra input to improve traditional recommendation systems without needing more information about the user or the items. Our method shows how LLMs can be used to make recommendation systems that are more logical and easier to understand through personalized reasoning graphs. LLMRG lets recommendations benefit from traditional recommendation systems and reasoning graphs created by LLMs. We show that LLMRG works well on standard measures and real-world situations to improve basic recommendation models. Tutorial on Large Language Models for Recommendation Foundation Models like Large Language Models (LLMs) have greatly improved many fields of study. They are especially useful for recommender systems, where they help provide personalized suggestions. LLMs can handle different tasks in recommendation systems, such as predicting ratings, recommending in sequence, making straightforward recommendations, and generating explanations. They do this by turning these tasks into language instructions. LLMs are also great at understanding natural language. This means they can understand what users prefer, descriptions of items, and the context of information to give better and more relevant suggestions. This leads to users being more satisfied and engaged. This tutorial will teach you about Foundation Models like LLMs for recommendation. We’ll talk about how recommender systems have evolved from simple models to complex ones and then to large models. We’ll also discuss how LLMs make it possible to generate recommendations, which is different from the traditional way of discriminating recommendations. We’ll also show you how to build recommender systems that use LLMs. We’ll cover many aspects of these systems, including preparing data, designing the model, pre-training the model, fine-tuning and prompting, learning from multiple modes and tasks, and trustworthy aspects of these systems, like fairness and transparency. Evaluations and limitations Pre-trained language models (PLMs) like BERT and GPT are good at understanding text and know much about the world. This makes them very good at adapting to different tasks. In this study, we suggest using these powerful PLMs as recommendation systems. We use prompts to change the task of recommending based on sessions into a multi-token cloze task, a fill-in-the-blank task. We tested this method on a dataset for recommending movies in zero-shot and fine-tuned settings. In the zero-shot setting, there is no training data available. We found that PLMs did much better than just recommending randomly. However, we also noticed a strong bias in language when using PLMs as recommenders. In the fine-tuned setting, there is some training data available. This reduced the bias in language, but PLMs didn’t do as well as traditional recommendation systems like GRU4Rec. Our findings show that there are opportunities for using this new method, but challenges also need to be addressed. Content Reference: https://arxiv.org/abs/2308.10837?source=post_page6cfad2cf9dc7 https://dl.acm.org/doi/abs/10.1145/3604915.3608889?source=post_page6cfad2cf9dc7 https://dl.acm.org/doi/abs/10.1145/3604915.3609494?source=post_page6cfad2cf9dc7
