Medical dialogue generation (MDG) has gained increasing attention due to its
substantial practical value. Previous works typically employ a
sequence-to-sequence framework to generate medical responses by modeling
dialogue context as sequential text with annotated medical entities. While
these methods have been successful in generating fluent responses, they fail to
provide process explanations of reasoning and require extensive entity
annotation. To address these limitations, we propose the method Bootstrap
Prompting for Explicit Reasoning in MDG (BP4ER), which explicitly model MDG's
multi-step reasoning process and iteratively enhance this reasoning process. We
employ a least-to-most prompting strategy to guide a large language model (LLM)
in explicit reasoning, breaking down MDG into simpler sub-questions. These
sub-questions build on answers from previous ones. Additionally, we also
introduce two distinct bootstrapping techniques for prompting, which
autonomously correct errors and facilitate the LLM's explicit reasoning. This
approach eliminates the need for entity annotation and increases the
transparency of the MDG process by explicitly generating the intermediate
reasoning chain. The experimental findings on the two public datasets indicate
that BP4ER outperforms state-of-the-art methods in terms of both objective and
subjective evaluation metrics.

通过引入 BP4ER 方法，我们成功地将医学对话生成过程中的推理链路显式生成，不仅提高了透明度，还在客观和主观评估指标方面优于现有方法。

BP4ER：医学对话生成中的引导推理引导

BP4ER: Bootstrap Prompting for Explicit Reasoning in Medical Dialogue  Generation

Semi-supervised bootstrapping techniques for relationship extraction from
text iteratively expand a set of initial seed instances. Due to the lack of
labeled data, a key challenge in bootstrapping is semantic drift: if a false
positive instance is added during an iteration, then all following iterations
are contaminated. We introduce BREX, a new bootstrapping method that protects
against such contamination by highly effective confidence assessment. This is
achieved by using entity and template seeds jointly (as opposed to just one as
in previous work), by expanding entities and templates in parallel and in a
mutually constraining fashion in each iteration and by introducing
higherquality similarity measures for templates. Experimental results show that
BREX achieves an F1 that is 0.13 (0.87 vs. 0.74) better than the state of the
art for four relationships.

BREX 是一种新的半监督自动关系抽取方法，通过高效的置信度评估使用实体和模板种子来防止污染问题。实验结果表明，BREX 的 F1 得分比现有技术高 0.13。