推理模型能更準確地表達其置信度

摘要

儘管大型語言模型（LLMs）具有強大的能力，但它們往往無法準確地表達其信心程度，這使得評估它們何時可能出錯變得困難，從而限制了其可靠性。在本研究中，我們展示了推理模型——即那些進行延伸的思維鏈（CoT）推理的LLMs——不僅在問題解決上表現優異，而且在準確表達其信心方面也顯著優於非推理模型。具體而言，我們在六個數據集上對六種推理模型進行了基準測試，發現它們在36種設置中的33種情況下，其信心校準表現均嚴格優於非推理模型。我們詳細的分析揭示，這些校準上的提升源於推理模型的慢思考行為——例如探索替代方法和回溯——這些行為使它們能夠在整個CoT過程中動態調整其信心，使其逐漸變得更加準確。特別是，我們發現推理模型的校準程度隨著CoT的展開而逐漸提高，這一趨勢在非推理模型中並未觀察到。此外，從CoT中移除慢思考行為會導致校準顯著下降。最後，我們表明這些提升並非推理模型所獨有——非推理模型在通過上下文學習引導進行慢思考時也能獲益。

English

Despite their strengths, large language models (LLMs) often fail to communicate their confidence accurately, making it difficult to assess when they might be wrong and limiting their reliability. In this work, we demonstrate that reasoning models-LLMs that engage in extended chain-of-thought (CoT) reasoning-exhibit superior performance not only in problem-solving but also in accurately expressing their confidence. Specifically, we benchmark six reasoning models across six datasets and find that they achieve strictly better confidence calibration than their non-reasoning counterparts in 33 out of the 36 settings. Our detailed analysis reveals that these gains in calibration stem from the slow thinking behaviors of reasoning models-such as exploring alternative approaches and backtracking-which enable them to adjust their confidence dynamically throughout their CoT, making it progressively more accurate. In particular, we find that reasoning models become increasingly better calibrated as their CoT unfolds, a trend not observed in non-reasoning models. Moreover, removing slow thinking behaviors from the CoT leads to a significant drop in calibration. Lastly, we show that these gains are not exclusive to reasoning models-non-reasoning models also benefit when guided to perform slow thinking via in-context learning.

推理模型能更準確地表達其置信度

Reasoning Models Better Express Their Confidence

摘要

Summary

Support

Support