Transparency in AI
Nathan emphasizes the critical need for transparency in language models, highlighting the challenges posed by the recent release of Croissant LLM and its implications for openness in machine learning. He critiques the tendency to game transparency metrics and laments the muddling of safety discussions, which have shifted focus from meaningful harms to broader, less specific concerns. The evolving narrative around safety presents both challenges and opportunities for open LLMs, particularly as the industry grapples with the consequences of past oversights.In this clip
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The koan of an open-source LLM
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