Tag: Reasoning
All the articles with the tag "Reasoning".
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Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs
本文提出了低秩知识遗忘(LoKU)框架,包含反向铰链损失(IHL)和 Fisher 加权低秩适配器初始化(FILA),以实现鲁棒且参数高效的大语言模型知识遗忘,有效移除敏感信息同时保持模型原有能力。
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LZ Penalty: An information-theoretic repetition penalty for autoregressive language models
本文提出LZ惩罚方法,基于LZ77压缩算法的码长变化动态调整自回归语言模型的采样分布,在贪婪解码下有效消除退化重复,同时保持推理基准性能。
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Waking Up an AI: A Quantitative Framework for Prompt-Induced Phase Transition in Large Language Models
本文提出了一种双重提示框架(TIP和TQP)来量化大型语言模型(LLMs)的认知相变,发现LLMs对概念融合提示的情感反应与人类直觉差异显著,揭示了AI与人类认知在概念整合上的潜在鸿沟。
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Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design
This position paper advocates for redesigning Large Language Models as 'reasonable parrots' that integrate argumentation theory principles to foster critical thinking through multi-persona dialogues, challenging users with diverse perspectives rather than providing one-sided answers.
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Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
本文提出PLAN-AND-ACT框架,通过分离规划和执行模块、利用合成数据训练和动态重规划,提高LLM代理在复杂长期任务中的性能,并在web导航基准上达到state-of-the-art结果。