Verily’s Post

Researchers have put together a hallucination leader board for popular large language models based on summarization of input https://lnkd.in/eYR_aJGB.  #generativeai #largelanguagemodel #hallucinations #leaderboard #artificialintelligence

Ultimately all AI models (either through self or imposed regulation) will easily be able to set “system prompts” that guide them to follow core human values like respect and empathy for others, not doing harm to others, etc. Fortunately, models can be more reliable (than humans) in staying on a “good path” especially if definitively told by their creators to never compromise on core principles.

Decoding the DNA of Large Language Models: A Comprehensive Survey on Datasets, Challenges, and Future Directions Developing and refining Large Language Models

Can Large Language Models Handle Longer Contexts Without Additional Training? This AI Paper Proposes SelfExtend to Stimulate LLMs Long Context Handling Potential Within large language models

Recent research makes it clear that hallucinations are inherently parts of LLMs. - Hallucination is Inevitable: An Innate Limitation of Large Language Models https://lnkd.in/e3Myh45s - Calibrated Language Models Must Hallucinate https://lnkd.in/e4WcmnFH #LLMs #generativeai #hallucinations

🚀 Enhancing Large Language Model Capabilities with Mixture-of-Agents (MoA) 🚀 Together AI https://lnkd.in/gBwHuiH6 https://lnkd.in/gfj2NTRx Exciting advancements in AI! The Mixture-of-Agents (MoA) methodology leverages the collective strengths of multiple Large Language Models (LLMs) to enhance natural language understanding and generation. 🌐23 🔍 Key Highlights: Collaborative Approach: MoA uses a layered architecture where each layer comprises multiple LLM agents4. Each agent refines responses based on outputs from previous layers, leading to more robust and comprehensive results5. State-of-the-Art Performance: MoA has achieved top scores on benchmarks like AlpacaEval 2.0, MT-Bench, and FLASK, surpassing even GPT-4 Omni6. Diverse Expertise: By harnessing the unique strengths of different LLMs, MoA creates a more capable and versatile model. This innovative approach is a significant step towards more powerful and efficient AI systems. 🌟 #AI #MachineLearning #NaturalLanguageProcessing #Innovation #Tech

List of Activities and Their Corresponding Suitable LLMs in the Artificial Intelligence AI World Right Now: A Comprehensive Guide Choosing large language models

A Practical Guide Hallucination of Multimodal Large Language Models https://lnkd.in/gxycvHta

List of Activities and Their Corresponding Suitable LLMs in the Artificial Intelligence AI World Right Now: A Comprehensive Guide Choosing large language models

How do we go about evaluating Large Language Models? Most approaches can be categorized into four groups: 🎯 Task-Specific Metrics 🔬 Research Benchmarks 🤖 LLM Self-Evaluation 👤 Human Evaluation Read more from Michał Oleszak's post: