kaikai luo’s Post

🚀 Symbolic Chain-of-Thought! 🤖✨ A new method has been proposed to enhance the logical reasoning capabilities of Large Language Models (LLMs) by integrating symbolic expressions and logical rules with Chain-of-Thought (CoT) prompting. This innovative approach is called Symbolic Chain-of-Thought (SymbCoT). 🔍 Key Features of Symbolic Chain-of-Thought: 1️⃣ Symbolic Translation: Translates natural language context into a symbolic format to better handle logical reasoning. 2️⃣ Step-by-Step Planning: Derives a detailed plan to solve problems using symbolic logical rules. 3️⃣ Verification Mechanism: Includes a verifier to check the translation and reasoning chain for accuracy. 💡 Insights from the Study: - Logical Reasoning: Enhances the reasoning capabilities of LLMs by incorporating symbolic expressions and rules, enabling more precise and explainable logical reasoning. - Framework: SymbCoT is a fully LLM-based framework that does not rely on external reasoners, making it robust against syntax errors and more human-understandable. - Improved Performance: Demonstrates significant improvements in logical reasoning tasks over traditional CoT methods. 📈 Performance Metrics: - Datasets: Thoroughly evaluated on five standard datasets using both First-Order Logic (FOL) and Constraint Optimization (CO) symbolic expressions. - Accuracy: Achieves higher accuracy rates compared to state-of-the-art solutions, showcasing better performance in complex logical reasoning tasks. 🌟 Why It Matters: - Faithful Reasoning: Ensures more faithful, flexible, and explainable logical reasoning, bringing LLMs closer to human-level reasoning capabilities. - Advanced AI Agents: Helps build more reliable and intelligent LLM-based AI agents for real-world applications. Stay tuned for more updates on this revolutionary AI development! 🚀 📊 Paper: https://lnkd.in/eEu3PV3A #AI #SymbCoT #MachineLearning #TechInnovation #LogicalReasoning #DataScience #DeepLearning

This research introduces a novel approach called "Analogical Prompting" for large language models (LLMs) to improve their reasoning abilities. The central problem addressed by this approach is the limitations of existing "Chain-of-Thought" (CoT) prompting, which requires pre-defined examples for guiding LLMs' reasoning and can be challenging to implement for various tasks ✨ . Paper Link: https://lnkd.in/dACtAzMj Central Problem 😥 : The central problem addressed is the limitation of existing "Chain-of-Thought" (CoT) prompting, which relies on pre-existing examples to guide LLMs in reasoning tasks. This presents two key challenges: providing suitable guidance/examples for reasoning and reducing the need for manual labeling of reasoning examples, which can be time-consuming and impractical for every task. Proposed Solution 😱 : The proposed solution is "Analogical Prompting." This approach empowers LLMs to autonomously generate relevant reasoning examples and knowledge before addressing a task. It leverages analogical reasoning, akin to how humans draw from past experiences to tackle new problems. Analogical Prompting offers the following advantages: 1️⃣ Eliminates the requirement for labeled reasoning examples, providing generality and convenience. 2️⃣ Tailors the generated examples and knowledge to each specific problem, offering adaptability. Methodology: The methodology draws inspiration from human analogical reasoning. When presented with a task, LLMs following Analogical Prompting are instructed to: 1️⃣ Generate pertinent examples (comprising problems and their solutions) related to the task. 2️⃣ Utilize these self-generated examples as guidance to solve the primary task. Results : Experimental testing of Analogical Prompting demonstrated superior performance compared to other methods: ➡ Outperformed 0-shot CoT (Chain-of-Thought) and manual few-shot CoT. ➡ Achieved improvements across a range of reasoning benchmarks, including: - Math problem solving (MATH, GSM8K). - Code generation (Codeforces). - Various reasoning tasks within BIG-Bench. In conclusion, Analogical Prompting offers a promising approach to improve LLM reasoning capabilities by generating customized exemplars for individual problems without needing labeled data, addressing the limitations of existing CoT prompting methods. follow for more Shaishav Surati 🇮🇳 #Ai #llm #analogicalreasoners #ML #CotPrompting #promptengineering

SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals 🏆 Conclusion: SELFGOAL is a powerful framework enabling LLMs to achieve high-level goals in dynamic environments, outperforming existing methods in competitive and cooperative scenarios. 🆕 Meet SELFGOAL: An innovative method enhancing language agents to achieve high-level goals with minimal human input and feedback. 🚀 SELFGOAL dynamically breaks down high-level goals into practical sub-goals through adaptive tree structures during interactions. 📈 Experiments show SELFGOAL significantly boosts language agents' performance in competitive, cooperative, and delayed feedback environments. 🔍 Background: Large Language Models (LLMs) enable autonomous agents to solve complex tasks, but achieving vague high-level goals remains challenging. 💡 Existing methods fall short by lacking real-time guidance during task execution or being unable to derive structured rules from experiences. 🌟 SELFGOAL builds and utilizes a Goal Tree (GOALTREE) to dynamically decompose tasks, ensuring agents stay on track towards main goals. 🧩 Methodology: SELFGOAL's modules—search, decomposition, and action—work together to guide LLMs in executing actions based on current states and goals. 🧪 Evaluated on tasks like public goods games, guessing games, auctions, and negotiations, SELFGOAL shows superior performance, especially with larger LLMs. 📊 Results: SELFGOAL outperforms baseline frameworks by aligning guidance with primary goals and maintaining clarity through GOALTREE's logical structure. 🔍 Module Analysis: The search module selects optimal sub-goals, and higher quality GOALTREEs from stronger models like GPT-4 offer better guidance. #AI #Innovation #MachineLearning #AIResearch #Tech #AIChallenges #Research #AIResults #TechInnovation

Trusting Language Models: Factors to Consider While Large Language Models (LLMs) are powerful tools, it’s crucial to approach their responses critically. Here are reasons why the same prompt can yield different outputs: Model DNA: Each LLM has a unique architecture and training data. Some excel at factual tasks, while others shine in creative writing. This background shapes their understanding and response style. Specialized Skills: Fine-tuned LLMs, like those for code generation, produce distinct outputs. General-purpose models differ in their responses. The Random Factor: LLMs inject randomness during generation, leading to non-deterministic variations. Prompting Precision: Nuances in phrasing impact how an LLM interprets a prompt. Precision matters. Developer Influences: Unintended biases from developers affect LLM interpretation and responses. Reminder: Be an informed user. Consider these factors when evaluating LLM outputs. Case in point: I explored top LLMs’ views on current industry’s top 10 AI chips - market share %, use, advantages, and disadvantages. The results were surprising more different that I expected. Like market share % and specific details of advantages or disadvantages of AI chip. #LLMs #AI #CriticalThinking

🚀 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

🚀 Exciting Breakthrough in AI Research! 🚀 A recent paper published on arXiv (https://lnkd.in/gGeHCTgP) reveals a groundbreaking approach using Large Language Models (LLMs) that has not only outperformed humans in long-form fact-checking tasks but also achieved this feat with significantly higher efficiency and accuracy. The competition results are in, and the numbers speak volumes: LLMs are 20 times cheaper and more accurate than traditional human-led methods. This monumental achievement isn’t just a win for efficiency; it’s a leap towards redefining how we interact with information in the digital age. But the implications stretch far beyond just fact-checking. The architecture and methodology proposed in this paper opens doors to a myriad of applications: 1. AI Governance: Implementing this technology could revolutionize policy-making and regulatory compliance, making it more data-driven and precise. 2. Enhanced Customer Interactions: Businesses can leverage these insights to provide more accurate, reliable, and cost-effective customer service solutions. 3. Continuous Improvement of LLMs: By incorporating factual accuracy checks, we can ensure that LLMs self-improve and evolve with the changing dynamics of data and information. Dive into the paper to explore the methodology, results, and potential applications of this new approach. Let’s discuss how we can leverage these insights to drive forward in AI governance, enhance customer interactions, and continue improving the factual accuracy of LLMs! #AI #Innovation #Technology #MachineLearning #FactChecking #LLMs #AIGovernance #CustomerService

How to Efficiently Fine-Tune Large Language Models (LLMs) Using PEFT Fine-tuning large language models (LLMs) can be a complex and resource-intensive task. However, using Parameter-Efficient Fine-Tuning (PEFT) techniques can significantly streamline the process. Here’s how you can efficiently fine-tune LLMs using PEFT. What is PEFT? PEFT is an approach that focuses on tuning only a small subset of the model’s parameters instead of the entire model. This makes the process faster, more cost-effective, and less computationally demanding. Key Benefits of PEFT 1. Reduced Computational Cost 2. Faster Training 3. Lower Memory Usage Steps to Efficiently Fine-Tune LLMs Using PEFT Identify Critical Layers: Determine which layers of the model have the most impact on performance for your specific task. Typically, these are the higher layers in the model. Freeze Unnecessary Parameters: Freeze the weights of less critical layers to reduce the number of parameters that need updating. This step is crucial for saving computational resources. Use a Smaller Learning Rate: Apply a smaller learning rate to the fine-tuning process to ensure stable updates and prevent overfitting. Leverage Pre-trained Models: Start with a pre-trained model that has already been trained on a large dataset. This allows you to benefit from the general language understanding it has acquired. Monitor Performance Closely: Regularly evaluate the model’s performance on a validation set to ensure that fine-tuning is improving the desired metrics without overfitting. Iterate and Refine: Fine-tuning is an iterative process. Continuously monitor, adjust, and refine the parameters and hyperparameters to achieve optimal performance. Attached is a comparison table between PEFT vs Traditional Fine-Tuning. Conclusion PEFT is a game-changer in the world of fine-tuning large language models. By efficiently managing resources and focusing on the most impactful parameters, you can achieve significant improvements in performance without the high costs and time investments typically associated with fine-tuning. #AI #MachineLearning #LLM #PEFT #FineTuning #TechInnovation #ArtificialIntelligence

📣 Excited to introduce Self-Instruct, a groundbreaking framework designed to supercharge 🚀 language models in understanding and executing natural language instructions! 🗨️🤖 📜 Background: Instruction-tuned language models are awesome 🌟 at understanding and performing tasks based on human-written guidelines. But, they often fall short 📉 due to the limited diversity and quality of manually annotated data. 🛠️ How Self-Instruct Works: Think of it as a self-feeding mechanism! 🔄 It starts with a seed of manual instructions 🌱 and uses these to prompt the model into generating its own, new instructions and examples. We filter out the fluff, add the good stuff back, and voila! We have a more efficient, instruction-following language model! 🎯 🔁 It's an iterative process, meaning each cycle makes the model smarter and more versatile, without needing endless human annotations! 🙌 🤖🧠 This way, we are taking giant leaps 🦘 in improving the adaptability and effectiveness of language models for real-world applications! #SelfInstruct #LanguageModels #NaturalLanguageProcessing #AI #MachineLearning #Automation #Innovation 🌐💡 (Source: https://lnkd.in/gNYeYBeX)

📚#H2O AI has recently released a #new #languageModel called #H2O-Danube-1.8B1. This model is a significant step towards democratizing access to AI, as it is an open-source natural language model with 1.8 billion parameters. #keywordresearch details about #H2O-Danube-1.8B are as follows: 🎯#Training: The model was trained on 1 trillion tokens collected from diverse web sources1. The techniques used for training were refined from models like LLama 2 and Mistral. 🎯#Performance: Despite the relatively limited training data, benchmark results show that H2O-Danube-1.8B performs on par or better than other models in the 1-2 billion parameter size class across tasks like common sense reasoning, reading comprehension, summarization, and translation. 🎯#ChatModel: A version of the model fine-tuned specifically for conversational applications, known as #H2O-Danube-1.8B-Chat, was also released1. This chat version is tuned using supervised learning on dialog datasets followed by reinforcement learning using human preferences. 🎯#Availability: Both the base #H2O-Danube-1.8B model and #chat-tuned version are available immediately from #HuggingFace. H2O.ai will be releasing additional #tools to simplify using the models in applications, as well as exploring potential future model scaling. This model demonstrates the capabilities and advantages of more modestly sized models, reaching high-performance benchmarks while remaining efficient, accessible, and responsible. #Danube #H20AI #LLM for #MobileApplications. #AInews #techblogs #HuggingFace source # Venture Beat

Can Long-Context Language Models Surpass Existing Paradigms? I’m excited to share some exciting insights from the paper “Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?” Here’s what I found fascinating: 1. LCLMs vs. Specialized Models: At the 128k token level, Long-Context Language Models (LCLMs) are on par with leading textual retrieval systems like Gecko and even surpass multi-modal models such as CLIP in some areas. 2. Strengths and Limitations: While LCLMs are making waves in retrieval and reasoning tasks, they still face challenges with complex multi-hop compositional reasoning, such as in SQL-like queries. There’s significant room for growth in these areas. 3. The Power of Prompting: Performance varies widely with different prompting strategies, highlighting the need for continued innovation in how we craft prompts to maximize LCLMs’ effectiveness. 4. The Future of AI: The research underscores that while LCLMs are closing in on specialized models, there’s still a lot of potential to unlock as we push the boundaries of context length and prompt engineering. You can find the paper link in comment section #machinelearning #airesearch #languagemodel #innovation #ai