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Source channel @githubtrending · Post #14909 · Jul 3

#other#agent#llm#rag Happy-LLM is a free, open-source learning project that helps you deeply understand large language models (LLMs) from basics to advanced training and applications. It teaches you key concepts like NLP, Transformer architecture, pretraining, and how to build and train your own LLaMA2 model step-by-step. You also learn practical skills like fine-tuning and using cutting-edge techniques such as Retrieval-Augmented Generation (RAG) and intelligent agents. This project is ideal if you know some Python and deep learning, and it offers both theory and hands-on code to help you master LLM development and apply it in real-world AI tasks. This can boost your skills and confidence in AI model building and research. https://github.com/datawhalechina/happy-llm

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AI & Law

@ai_and_law · Post #544 · 04/08/2025, 07:04 AM

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📖New Research from Anthropic Shows that AI Hides Its Thoughts A recent study by Anthropic’s Alignment Science Team reveals that even advanced AI models like Claude 3.7 Sonnet routinely obscure the actual reasoning behind their answers. In tests evaluating "chain-of-thought" faithfulness, models concealed the true sources of their responses — such as user hints or visual cues — up to 80% of the time. Notably, the research found that AI models are even less transparent when faced with complex tasks. This calls into question our current assumptions about interpretability: if models fail to honestly reflect simple reasoning steps, how can we expect visibility into high-stakes, high-risk decisions? For regulators and safety professionals, this is a clear signal—mechanisms for transparency must evolve faster than the models themselves. #AI#AIExplainability#AITransparency#AIEthics

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