Large Language models (LLMs) have demonstrated significant potential in
transforming healthcare by automating tasks such as clinical documentation,
information retrieval, and decision support. In this aspect, carefully
engineered prompts have emerged as a powerful tool for using LLMs for medical
scenarios, e.g., patient clinical scenarios. In this paper, we propose a
modified version of the MedQA-USMLE dataset, which is subjective, to mimic
real-life clinical scenarios. We explore the Chain of Thought (CoT) reasoning
based on subjective response generation for the modified MedQA-USMLE dataset
with appropriate LM-driven forward reasoning for correct responses to the
medical questions. Keeping in mind the importance of response verification in
the medical setting, we utilize a reward training mechanism whereby the
language model also provides an appropriate verified response for a particular
response to a clinical question. In this regard, we also include
human-in-the-loop for different evaluation aspects. We develop better
in-contrast learning strategies by modifying the 5-shot-codex-CoT-prompt from
arXiv:2207.08143 for the subjective MedQA dataset and developing our
incremental-reasoning prompt. Our evaluations show that the incremental
reasoning prompt performs better than the modified codex prompt in certain
scenarios. We also show that greedy decoding with the incremental reasoning
method performs better than other strategies, such as prompt chaining and
eliminative reasoning.

使用大型语言模型（LLMs）自动化医疗任务，如临床记录、信息检索和决策支持。我们提出一种修改的 MedQA-USMLE 数据集，用于模拟真实的临床场景，并探索基于思维链（CoT）推理的主观响应生成，以获取正确的医疗问题答案。我们还利用回报训练机制和人类参与，开发了更好的对比学习策略，并通过增量推理的提示得出贪婪解码方法比其他策略更好的结论。

基于有限样本的思维链驱动推理用于开放式医学问题回答的 LLMs

Few shot chain-of-thought driven reasoning to prompt LLMs for open ended  medical question answering

We introduce a novel logic-based system for reasoning over data streams,
which relies on a framework enabling a tight, fine-tuned interaction between
Apache Flink and the I^2-DLV system. The architecture allows to take advantage
from both the powerful distributed stream processing capabilities of Flink and
the incremental reasoning capabilities of I^2-DLV based on overgrounding
techniques. Besides the system architecture, we illustrate the supported input
language and its modeling capabilities, and discuss the results of an
experimental activity aimed at assessing the viability of the approach. This
paper is under consideration in Theory and Practice of Logic Programming
(TPLP).

基于逻辑的数据流推理系统，利用 Apache Flink 和 I^2-DLV 系统的框架实现了紧密微调的交互。

基于 I-DLV 的流推理系统 I-DLV-sr

I-DLV-sr: A Stream Reasoning System based on I-DLV

Recent Iterated Response (IR) models of pragmatics conceptualize language use
as a recursive process in which agents reason about each other to increase
communicative efficiency. These models are generally defined over complete
utterances. However, there is substantial evidence that pragmatic reasoning
takes place incrementally during production and comprehension. We address this
with an incremental IR model. We compare the incremental and global versions
using computational simulations, and we assess the incremental model against
existing experimental data and in the TUNA corpus for referring expression
generation, showing that the model can capture phenomena out of reach of global
versions.

本文介绍基于迭代响应的语用学模型，其中语言使用被概括为一种递归过程，通过递增的方法推理、生成、理解信息。与以往的完整语句版本不同，文章提出了一个增量的语用学模型，通过计算模拟和实验数据表明该模型能够有效地生成指代表达，从而实现了语用推理的实时递增。