IJCAI International Joint Conferences on Artificial Intelligence Organization’s Post

Do miracles happen when we exponentially increase size of Large Language Models(LLM)? When there is major increase in size of LLM, it performs tasks which it was not expected to perform! For example, early GPT4 version, was trained on language only, & hence was expected to do activities like comprehension, text generation etc. Surprisingly it also did tasks which were not language dependent like vision, coding, mathematics effectively! Similarly, PaLM model showed exponential rise in Natural Language Understanding capability, when computational power during training is increased exponentially! Global AI community is yet to decipher the reason behind this unexpected behavior of LLMs! Like humans who are good in performing multiple tasks, some of these large language models are showing signs of performing a wide variety of tasks effectively! This is known as Artificial General Intelligence (AGI), were one model can perform a wide variety of tasks effectively just like a human! Some globally renowned AI experts including Sam Altman believe that AGI will arrive in 10-20 years from now! It's remarkable to see signs of it already! What do you think about this new phenomenon unfolding? #largelanguagemodels #artificialgeneralintelligence

Announcing LLaVA : An Open-Source LLM with Enhanced Vision Capabilities I'm excited to share LLaVA, a powerful open-source large language model (LLM) with impressive visual abilities! LLaVA can understand and respond to both text and image inputs, making it ideal for tasks requiring nuanced visual comprehension. LLaVA is a valuable tool for various applications, including image description, visual question answering, and data analysis. Get started with LLaVA today and explore its potential to transform your projects! You can try it here: https://lnkd.in/gsCJY2UR #llava #opensource #machinelearning #artificialintelligence #computer vision #nlp

How to mitigate hallucinations in Large Language Models (#LLMs)? 1. Knowledge Base Integration: Incorporate domain-specific knowledge bases to provide accurate, contextual information. 2. Chunk Optimization: Break down large documents into smaller, manageable chunks for better context retention. 3. Prompt Engineering: Craft precise prompts to guide the LLM towards accurate responses. 4. API Parameter Tuning: Adjust parameters like temperature and top_p to control output randomness. 5. Domain-Specific Classification: Implement classifiers to categorize queries and route them to appropriate models or knowledge bases. 6. Retrieval-Augmented Generation (RAG): Use RAG techniques to enhance responses with relevant, factual information. 7. Ensemble Approaches: Combine multiple models or techniques to improve overall accuracy and reliability. 8. Continuous Monitoring: Implement robust monitoring systems to detect and address hallucinations in real-time. 9. Feedback Loops: Establish mechanisms for user feedback to continuously improve the model’s performance. 10. Evaluation Frameworks: Utilize tools like #RAGAS and #TruLens to assess and improve the quality of LLM outputs. https://lnkd.in/gX54GwVC #AIOptimization #LLMTuning #NLPTech #MachineLearning #AIInnovation #LanguageModels #TextGeneration #DeepLearning #ArtificialIntelligence

💡 Now reading:VLMs are blind 🚀 Abstract: Large language models with vision capabilities (VLMs), e.g., GPT-4o and Gemini-1.5 Pro are powering countless image-text processing applications and scoring high on existing vision-understanding benchmarks. Yet, we find that VLMs fail on 7 visual tasks absurdly easy to humans such as identifying (a) whether two circles overlap; (b) whether two lines intersect; (c) which letter is being circled in a word; and (d) counting the number of circles in an Olympic-like logo. The shockingly poor performance of four state-of-the-art VLMs suggests their vision is, at best, like that of a person with myopia seeing fine details as blurry, and at worst, like an intelligent person who is blind making educated guesses. 🌐 https://lnkd.in/ekABJdDs 📄 https://lnkd.in/ewBHnRGH #vlm #deeplearning #machinelearning #visionlanguagemodels #artificialintelligence

Hey folks! Sharing with you some insights from level 3 of the GenAl Bootcamp by GTech MuLearn X Pathway 🌟 Let's dive into the fascinating world of language understanding, fueled by these key learnings: 1. Word Vectors: Ever wondered how machines understand words? Word vectors are the key! These numerical representations decode language, transforming it into a format machines can process effortlessly. Google's Word2Vec project, utilizing neural networks, maps words into a multi-dimensional space, revealing their semantic relationships. 2. Context Matters: Context is vital for large language models like ChatGPT and Bard. They rely on the "attention" mechanism to grasp the context of prompts, ensuring their responses are accurate and insightful. Sending prompts involves tokenization and detokenization – breaking down and reconstructing text for seamless communication. 3. Dynamic Embeddings: Dynamic embeddings, as seen in models like BERT and GPT-3, adjust word representations based on context, enriching their understanding of language through nuanced meanings in different linguistic environments. Contextual word vectors capture diverse meanings of a word across different contexts, enhancing system performance across applications. 4. Enhancing Semantic Understanding: Word embeddings play a crucial role in NLP tasks such as text classification and sentiment analysis, capturing semantic similarities crucial for accurate analysis. Techniques like Principal Component Analysis (PCA) simplify word vector spaces, balancing dimensionality to efficiently capture semantic nuances. #pathway #mulearn

RAG + LLM Survey: Towards Retrieval-Augmented Large Language Models The paper "A Survey on RAG Meets LLMs: Towards Retrieval-Augmented Large Language Models" discusses the integration of Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs). It provides a comprehensive review of the technical aspects, including architectures, training paradigms, and application tasks of RA-LLMs. The survey begins by outlining the advancements and classifications of LLMs into three categories: Encoder-only (e.g., BERT), Decoder-only (e.g., GPT), and Encoder-Decoder models (e.g., T5). It then delves into prompt learning techniques, emphasizing manual prompt engineering, soft prompt tuning, and in-context learning. The core focus is on the RAG framework, which enhances LLMs by integrating retrieval processes to provide relevant external knowledge. This framework involves three main processes: retrieval, generation, and augmentation. The retrieval component is discussed in detail, highlighting sparse and dense retrieval methods, pre- and post-retrieval enhancements, and the construction of retrieval databases. The paper compares related surveys, noting its unique focus on systematically reviewing RA-LLMs based on technical perspectives and their application tasks. Read the complete paper: https://lnkd.in/dmTBt78x Image credits: Fan et al (2024), (ArXiv paper in the reference) #ai #genAI #LLM #RAG

🎉 Exciting news! I'm thrilled to announce the launch of my new blog, LLM Hub !!! https://lnkd.in/ejpCvBmh As someone deeply fascinated by Large Language Models, I found myself constantly trying to keep up with the rapid advancements in this field. LLM Hub is my way of organizing and sharing what I'm learning, hoping it might help others who are also trying to navigate this complex landscape. The blog focuses on: 🧠 Breaking down complex LLM concepts. 🔬 Analyzing cutting-edge techniques. 💡 Providing practical insights for NLP practitioners. My first post dives into Masked Language Modeling, covering: 1. 6 key techniques from BERT to XLNet 2. Comparative analysis of these approaches 3. Unlocking the secrets of choosing the perfect masking strategy 🗝️ You can read the full article on: 1. LLM Hub: https://lnkd.in/ee6ack_2 2. Medium: https://lnkd.in/dG52_MBB LLM Hub isn't just about information dumping. My goal is to: 🔍 Demystify the intricacies of LLMs. 🌐 Explore real-world applications and implications . 📈 Stay updated on the latest developments. I'm committing to three posts per week, each exploring different aspects of LLMs. Writing about these topics helps me solidify my own understanding, and I hope it can be a useful resource for others in the field. 📝 Follow my writing journey: LLM Hub Website: https://lnkd.in/ejpCvBmh Medium Profile: https://lnkd.in/dGZ2XTif All my articles will be available on both platforms, so you can choose your preferred reading experience! If you're curious about the inner workings of LLMs or looking for practical insights, I'd love for you to check out my content and join me on this learning adventure. Let's embark on this AI learning journey together! 🤖📚 #LLMHub #NLP #AILearningJourney #LanguageModels #MachineLearning #MediumArticle

🚀 Exciting News! 🚀 I’m very excited to share our latest work, "ShareLoRA: Parameter Efficient and Robust Large Language Model Fine-tuning via Shared Low-Rank Adaptation," now available on arXiv: https://lnkd.in/gPY5Hu-7 🔍 In this paper, we introduce ShareLoRA, an approach to parameter-efficient fine-tuning large language models that is not only more parameter efficient but also enhances robustness. 👥 This paper is the result of a collaborative effort, and I want to express my gratitude to all co-authors and contributors for their hard work and dedication. 💡 We believe that ShareLoRA can make a significant impact on how we fine-tune, making advanced AI more adaptive and efficient. ✨ I'm eager to hear your thoughts and feedback! Let's push the boundaries of what's possible together! #AI #MachineLearning #LanguageModels #ParameterEfficientFinetuning

🟣GNN-RAG: A Novel AI Method for Combining Language Understanding Abilities of LLMs with the Reasoning Abilities of GNNs in a Retrieval-Augmented Generation (RAG) Style ▪️LLMs possess extraordinary natural language understanding capabilities, primarily derived from pretraining on extensive textual data. However, their adaptation to new or domain-specific knowledge is limited and can lead to inaccuracies. Knowledge Graphs (KGs) offer structured data storage, aiding in updates and facilitating tasks like Question Answering (QA). ▪️ Retrieval-augmented generation (RAG) frameworks enhance #LLM performance by integrating KG information, which is crucial for accurate responses in QA tasks. Retrieval methods relying solely on LLMs struggle with complex graph information, hindering performance in multi-hop KGQA.

✅Exploring the Hierarchy of Large Language Model (LLM) Applications The pyramid diagram showcases the different levels of Large Language Model (LLM) applications from the foundational level of information retrieval to the advanced concept of an LLM operating system. The Pyramid of LLM Applications: 1. Information Retrieval 📚       🌟   Functionality: The foundation of LLMs, excelling at answering questions based on vast training data. 💡         🌟  Use Case: Ideal for simple queries and information retrieval.       2. Chatbot 💬     🌟  Functionality: Engaging in meaningful conversations and maintaining context.         🌟 Use Case: Perfect for customer support, personal assistants, and interactive engagements.       3. Retrieval Augmented Generation (RAG)      🌟 Functionality: Combining LLMs with retrieval mechanisms to enhance responses with real time or specific data.         🌟 Use Case: Great for applications requiring up to date information and personalized recommendations.       4. Agents 🤖      🌟 Functionality: Autonomous entities that leverage LLMs to perform complex tasks and make decisions on behalf of users, acting as intelligent assistants across various domains         🌟Use Case: Personal and business assistants that not only inform but also take action, such as booking appointments or automating workflows.       5. LLM Operating System (OS) 🖥️       🌟 Functionality: LLMs functioning as a comprehensive platform or operating system.        🌟 Use Case:Imagining systems that coordinate diverse applications and tasks across multiple fields, harmonizing various agents to deliver a seamless user experience.      As LLMs continue to evolve, it is fascinating to explore how these applications can shape various industries and streamline our interactions with technology. #LLM #LanguageModels #AI #MachineLearning #NLP #Chatbots #InformationRetrieval #RetrievalAugmentedGeneration #Agents #OperatingSystems #TechnologyTrends #InnovationStrategy #FutureOfWork #CustomerExperience #Automation #PersonalizedRecommendations #DataDrivenDecisions #GenAI #RAG