4/ Dataset for LLMs – a comprehensive overview (180+ pages) and analysis of LLM datasets.
@dair_ai
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BitNet b1.58: High-Performance 1-bit LLM Architecture
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3/ The Era of 1-bit LLMs – introduces a high-performing and cost-effective 1-bit LLM variant called BitNet b1.58 where every parameter is a ternary {-1, 0, 1}.
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Mistral Large: Multilingual LLM with Advanced Reasoning and Code
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2/ Mistral Large – a new LLM with strong multilingual, reasoning, maths, and code generation capabilities.
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LLM Agents Demonstrate Autonomous Website Hacking Capabilities
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10/ LLM Agents can Hack – shows that LLM agents can automatically hack websites and perform tasks like SQL injections without human feedback or explicit knowledge about the vulnerability beforehand.
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Survey of Major LLM Families: GPT, Llama, PaLM
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9/ Survey of LLMs – reviews three popular families of LLMs (GPT, Llama, PaLM), their characteristics, contributions, and limitations; includes a summary of capabilities and techniques developed to build and augment LLM.
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ChemLLM: Specialized LLM Outperforms GPT-4 Chemistry Tasks
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8/ ChemLLM – a dedicated LLM trained for chemistry-related tasks; claims to outperform GPT-3.5 on principal tasks such as name conversion, molecular caption, and reaction prediction; it also surpasses GPT-4 on two of these tasks.
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OS-Copilot Framework Builds Generalist Computer Agents
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6/ OS-Copilot – a framework to build generalist computer agents that interface with key elements of an operating system like Linux or MacOS; it also proposes a self-improving embodied agent for automating general computer tasks.
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TestGen-LLM Uses AI to Improve Software Testing Coverage
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7/ TestGen-LLM – uses LLMs to improve existing human-written tests; reports that after an evaluation on Reels and Stories products for Instagram, 75% of TestGen-LLM's test cases were built correctly, 57% passed reliably, and 25% increased coverage.
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Gemini 1.5: Compute-Efficient Multimodal Mixture-of-Experts Model
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2/ Gemini 1.5 – a compute-efficient multimodal mixture-of-experts model with capabilities such as recalling and reasoning over long-form content; reasons over long documents potentially containing millions of tokens, including hours of video and audio.https://t.co/oZyOtMNBnR
— DAIR.AI (@dair_ai) 18 février 20242/ Gemini 1.5 – a compute-efficient multimodal mixture-of-experts model with capabilities such as recalling and reasoning over long-form content; reasons over long documents potentially containing millions of tokens, including hours of video and audio.
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V-JEPA: Self-Supervised Vision Model Trained on Two Million Videos
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3/ V-JEPA – vision models trained on a feature prediction objective using 2 million videos; relies on self-supervised learning and doesn’t use pretrained image encoders, text, negative examples, reconstruction, or other supervision sources.