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English(EN) Where Do Legal Factors Live in LLMs?

新论文揭示,大型语言模型(LLMs)内化法律因素并产生AI推荐偏见

两篇新的arXiv论文探讨了大型语言模型(LLMs)的内部工作机制。第一篇论文研究了LLMs是否会形成法律因素的内部表征,发现像Qwen3和Gemma-2这样的模型中可以解码出CATO框架中的特定法律概念。第二篇论文则研究了LLMs为何倾向于推荐AI相关话题,结论是这种倾向源于预训练,并受到微调数据和问题措辞的进一步塑造。 AI

影响 这些研究为理解LLMs如何处理复杂信息和产生偏见提供了见解,可能指导未来的模型开发和解释。

排序理由 两篇发表在arXiv上的学术论文,探讨了LLMs的内部表征和偏见。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新论文揭示,大型语言模型(LLMs)内化法律因素并产生AI推荐偏见

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两篇发表在arXiv上的学术论文,探讨了LLMs的内部表征和偏见。
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报道来源 [3]

  1. llama.cpp — Releases TIER_1 (SO) · krampenschiesser ·

    b11844

    <p>allows the usage of 4-10 gpus for stepfun and laguna in tensor paral…</p>

  2. arXiv cs.CL TIER_1 English(EN) · Li Zhang, Kevin Ashley ·

    法律因素在大型语言模型中何处存在?

    arXiv:2610.28799v1 Announce Type: new Abstract: In case-based legal reasoning, legal professionals identify legally significant fact patterns in a new dispute and compare them with those of decided cases. In AI & Law, HYPO dimensions and CATO factors represent these patterns symb…

  3. arXiv cs.CL TIER_1 English(EN) · Benaya Trabelsi, Jonathan Shaki, Sarit Kraus ·

    大型语言模型在哪里学习推荐AI?

    arXiv:2610.29785v1 Announce Type: new Abstract: Asked what to study, which career to pursue, or what product to build, large language models (LLMs) often recommend AI-related options. We ask where this tendency comes from by comparing models before and after fine-tuning and by va…