CHAIR——幻觉分类器作为改进器
CHAIR -- Classifier of Hallucination as Improver
AI总结：
CHAIR通过分析各层令牌logits的紧凑特征来检测幻觉，在TruthfulQA和MMLU上显著提升零样本检测准确率，并启发利用内部表示改进解码策略。
摘要：
在这项工作中，我们提出了CHAIR（幻觉分类器作为改进器），一种通过分析每个令牌每一层的内部logits来检测幻觉的监督框架。我们的方法从所有层的令牌logits中提取一组紧凑的特征，如最大值、最小值、均值、标准差和斜率，从而在不发生过拟合的情况下实现有效的幻觉检测。在TruthfulQA和MMLU数据集上的实验表明，CHAIR显著提高了检测准确性，尤其是在零样本场景中，展示了其鲁棒性和泛化能力。除了幻觉检测之外，CHAIR还凸显了利用内部表示设计先进解码策略的潜力。通过利用logits中的模式，我们建议更复杂的模型和自适应解码方法可以进一步减少幻觉并提高文本完成质量。CHAIR不仅为检测幻觉提供了实用解决方案，还为探索LLM中更丰富的表示以改善其事实性和连贯性奠定了基础。
英文摘要：
In this work, we introduce CHAIR (Classifier of Hallucination As ImproveR), a supervised framework for detecting hallucinations by analyzing internal logits from each layer of every token. Our method extracts a compact set of features such as maximum, minimum, mean, standard deviation, and slope-from the token logits across all layers, enabling effective hallucination detection without overfitting. Experiments on TruthfulQA and MMLU datasets demonstrate that CHAIR significantly improves detection accuracy, particularly in zero-shot scenarios, showcasing its robustness and generalizability. Beyond hallucination detection, CHAIR highlights the potential of using internal representations for designing advanced decoding strategies. By leveraging patterns in logits, we suggest that more sophisticated models and adaptive decoding methods could further reduce hallucinations and enhance text completion quality. CHAIR not only offers a practical solution for detecting hallucinations but also lays the groundwork for exploring richer representations in LLMs to improve their factuality and coherence.
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。
