论文标题
使用大型语言模型的忠实推理
Faithful Reasoning Using Large Language Models
论文作者
论文摘要
尽管当代的大语言模型（LMS）表现出令人印象深刻的提问功能，但它们的答案通常是单个呼叫模型的产物。这需要不受欢迎的不透明度并损害绩效，尤其是在固有多步骤的问题上。为了解决这些局限性，我们可以通过一个过程通过因果结构反映了问题的基本逻辑结构的过程来展示如何制作LMS来执行忠实的多步推理。我们的方法是通过将推理步骤融合在一起的，每个步骤都来自调用两个微调的LMS，一个用于选择，一种用于推理，以产生有效的推理跟踪。我们的方法在推理轨迹的空间中进行了光束搜索，以提高推理质量。我们证明了模型对多步逻辑推论和科学提问的有效性，表明它在最终答案的准确性上的表现优于基准，并生成可解释的人类解释的推理痕迹，用户可以检查其有效性。
Although contemporary large language models (LMs) demonstrate impressive question-answering capabilities, their answers are typically the product of a single call to the model. This entails an unwelcome degree of opacity and compromises performance, especially on problems that are inherently multi-step. To address these limitations, we show how LMs can be made to perform faithful multi-step reasoning via a process whose causal structure mirrors the underlying logical structure of the problem. Our approach works by chaining together reasoning steps, where each step results from calls to two fine-tuned LMs, one for selection and one for inference, to produce a valid reasoning trace. Our method carries out a beam search through the space of reasoning traces to improve reasoning quality. We demonstrate the effectiveness of our model on multi-step logical deduction and scientific question-answering, showing that it outperforms baselines on final answer accuracy, and generates humanly interpretable reasoning traces whose validity can be checked by the user.
