让大模型的解释有理可循，答案与解释由同一推理过程生成。
Reasoning-Grounded Natural Language Explanations for Language Models
- 用推理序列作为上下文，生成可信的自然语言解释。
- 答案与解释高度一致，模型常直接复制推理中的中间决策。
- 推理过程提升答案质量，适合需要透明解释的场景。
我们提出一种大语言模型可解释性技术，通过将推理过程转化为词元序列并融入模型上下文，使模型在生成最终答案或解释时，能够解码出自然语言形式的解释。为提高解释的可信度，采用联合预测-解释方法，使答案和解释均直接从推理序列推导得出，互不依赖。实验表明，在多个问题领域中，该方法显著提升了答案与解释的一致性；模型常直接复制推理过程中的部分决策至答案或解释中。此外，基于推理的生成方式还改善了答案质量。
原文摘要 · Abstract (English)
We propose a large language model explainability technique for obtaining faithful natural language explanations by grounding the explanations in a reasoning process. When converted to a sequence of tokens, the outputs of the reasoning process can become part of the model context and later be decoded to natural language as the model produces either the final answer or the explanation. To improve the faithfulness of the explanations, we propose to use a joint predict-explain approach, in which the answers and explanations are inferred directly from the reasoning sequence, without the explanations being dependent on the answers and vice versa. We demonstrate the plausibility of the proposed technique by achieving a high alignment between answers and explanations in several problem domains, observing that language models often simply copy the partial decisions from the reasoning sequence into the final answers or explanations. Furthermore, we show that the proposed use of reasoning can also improve the quality of the answers.
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