从大型语言模型中的序列内部分散性中学习不确定性
Learning Uncertainty from Sequential Internal Dispersion in Large Language Models
AI总结：
本文提出SIVR框架，通过利用隐藏状态中的token和层级特征，以更基本的假设来估计不确定性，从而提高hallucination检测的准确性和泛化能力。
摘要：
不确定性估计是检测大型语言模型（LLMs）幻觉的一种有前途的方法。最近的方法通常依赖于模型内部状态来估计不确定性。然而，它们在层间隐藏状态演变上有严格假设，并且仅关注最后或平均token导致信息丢失。为了解决这些问题，我们提出了序列内部方差表示（SIVR），一个监督幻觉检测框架，利用隐藏状态中token级和层级的特征。SIVR采用更基本的假设，即不确定性体现在跨层内部表示的分散程度或方差中，而不是依赖特定假设，这使得方法模型和任务无关。它还聚合了每个token方差特征的完整序列，学习指示事实错误的时间模式，从而防止信息丢失。实验结果表明，SIVR在大多数情况下优于强大的基线。最重要的是，SIVR具有更强的泛化能力和避免依赖大规模训练集的能力，突显了其在实际部署中的潜力。我们的代码库可在https://github.com/ponhvoan/internal-variance上找到。
英文摘要：
Uncertainty estimation is a promising approach to detect hallucinations in large language models (LLMs). Recent approaches commonly depend on model internal states to estimate uncertainty. However, they suffer from strict assumptions on how hidden states should evolve across layers, and from information loss by solely focusing on last or mean tokens. To address these issues, we present Sequential Internal Variance Representation (SIVR), a supervised hallucination detection framework that leverages token-wise, layer-wise features derived from hidden states. SIVR adopts a more basic assumption that uncertainty manifests in the degree of dispersion or variance of internal representations across layers, rather than relying on specific assumptions, which makes the method model and task agnostic. It additionally aggregates the full sequence of per-token variance features, learning temporal patterns indicative of factual errors and thereby preventing information loss. Experimental results demonstrate SIVR consistently outperforms strong baselines. Most importantly, SIVR enjoys stronger generalisation and avoids relying on large training sets, highlighting the potential for practical deployment. Our code repository is available online at https://github.com/ponhvoan/internal-variance.
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。
