Probability is Not Enough: Exploring and Counting Divergent Tokens for Reasoning Uncertainty Quantification in LLMs· 概率不足：探索和计数分歧标记
As the chain-of-thought reasoning capabilities of large language models improve, evaluating and calibrating their reasoning confidence is becoming increasingly important for quantifying the uncertainty of their answers. Current methods for estimating the confidence of large language models are generally based on probabilities of selected key tokens, but the underlying mechanism remains unclear. Our pilot study finds that replacing selected token probabilities with coarse substitutes can also improve calibration, motivating us to further explore effective signals of model confidence. We introduce Divergent Token Confidence (DTC), a framework that estimates confidence by counting tokens at which two models strongly disagree during decoding. DTC identifies these divergent tokens using the Jen
探索两种模型解码时的分歧标记以量化大模型推理不确定性。
- 核心方法
- 提出DTC框架，通过解码过程中两模型强烈分歧的标记数量来评估模型信心。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有大模型推理信心评估方法依赖概率，但机制不明确且校准效果有限。
- 关键实验
- 通过一系列实验验证了DTC框架的有效性，展示其在提高模型信心评估上的优势。
- 主要贡献
- 提供了一种新的量化模型推理不确定性的方法，提高了模型信心校准的准确性。
- 意义与局限
- 有助于更好地理解和评估大模型的推理能力，但局限于特定类型的任务和模型。