LLM-CAS: 动态神经元扰动用于实时幻觉修正
LLM-CAS: Dynamic Neuron Perturbation for Real-Time Hallucination Correction
AI总结：
LLM-CAS通过动态神经元扰动实现实时幻觉修正，提升大型语言模型的事实准确性。
摘要：
大型语言模型（LLMs）常常生成缺乏事实或上下文支撑的幻觉内容，限制了其在关键应用中的可靠性。现有方法如监督微调和人类反馈强化学习需要大量数据和计算资源，而静态参数编辑方法在处理依赖上下文的错误和灾难性遗忘方面存在困难。我们提出了LLM-CAS框架，将实时幻觉修正建模为分层强化学习问题。LLM-CAS训练一个代理学习策略，使其在推理过程中根据当前上下文动态选择临时神经元扰动。与以往依赖启发式或预定义调整的动态方法不同，这种策略驱动的机制实现了自适应和精细的修正，而无需永久修改参数。在多个语言模型上的实验表明，LLM-CAS在事实准确性方面持续提升，其在StoryCloze上的提升为10.98个百分点，在TriviaQA上的提升为2.71个百分点，在TruthfulQA的MC1得分上的提升为2.06个百分点。这些结果优于静态编辑方法如ITI和CAA以及动态SADI框架。总体而言，LLM-CAS为提高LLMs的可靠性提供了一种高效且上下文感知的解决方案，具有未来多模态扩展的潜力。
英文摘要：
Large language models (LLMs) often generate hallucinated content that lacks factual or contextual grounding, limiting their reliability in critical applications. Existing approaches such as supervised fine-tuning and reinforcement learning from human feedback are data intensive and computationally expensive, while static parameter editing methods struggle with context dependent errors and catastrophic forgetting. We propose LLM-CAS, a framework that formulates real-time hallucination correction as a hierarchical reinforcement learning problem. LLM-CAS trains an agent to learn a policy that dynamically selects temporary neuron perturbations during inference based on the current context. Unlike prior dynamic approaches that rely on heuristic or predefined adjustments, this policy driven mechanism enables adaptive and fine grained correction without permanent parameter modification. Experiments across multiple language models demonstrate that LLM-CAS consistently improves factual accuracy, achieving gains of 10.98 percentage points on StoryCloze, 2.71 points on TriviaQA, and 2.06 points on the MC1 score of TruthfulQA. These results outperform both static editing methods such as ITI and CAA and the dynamic SADI framework. Overall, LLM-CAS provides an efficient and context aware solution for improving the reliability of LLMs, with promising potential for future multimodal extensions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
