The integration of Large Language Models (LLMs) into healthcare diagnostics
offers a promising avenue for clinical decision-making. This study outlines the
development of a novel method for zero-shot/few-shot in-context learning (ICL)
by integrating medical domain knowledge using a multi-layered structured
prompt. We also explore the efficacy of two communication styles between the
user and LLMs: the Numerical Conversational (NC) style, which processes data
incrementally, and the Natural Language Single-Turn (NL-ST) style, which
employs long narrative prompts. Our study systematically evaluates the
diagnostic accuracy and risk factors, including gender bias and false negative
rates, using a dataset of 920 patient records in various few-shot scenarios.
Results indicate that traditional clinical machine learning (ML) models
generally outperform LLMs in zero-shot and few-shot settings. However, the
performance gap narrows significantly when employing few-shot examples
alongside effective explainable AI (XAI) methods as sources of domain
knowledge. Moreover, with sufficient time and an increased number of examples,
the conversational style (NC) nearly matches the performance of ML models. Most
notably, LLMs demonstrate comparable or superior cost-sensitive accuracy
relative to ML models. This research confirms that, with appropriate domain
knowledge and tailored communication strategies, LLMs can significantly enhance
diagnostic processes. The findings highlight the importance of optimizing the
number of training examples and communication styles to improve accuracy and
reduce biases in LLM applications.

通过结合医学领域知识，采用多层结构的提示方法实现零 / 少样本上下文学习，并探索用户与大型语言模型的两种交流方式对诊断准确性和风险因素的影响。结果表明，大型语言模型通过域知识和定制化沟通策略能够显著提高诊断流程的准确性。研究还强调了在大型语言模型应用中优化训练样本数量和交流方式以提高准确性并减少偏见的重要性。

XAI4LLM。让机器学习模型和 LLM 合作，提升医疗领域的上下文学习

XAI4LLM. Let Machine Learning Models and LLMs Collaborate for Enhanced  In-Context Learning in Healthcare

Automated Program Repair (APR) aims to automatically generate patches for
buggy programs. Recent APR work has been focused on leveraging modern Large
Language Models (LLMs) to directly generate patches for APR. Such LLM-based APR
tools work by first constructing an input prompt built using the original buggy
code and then queries the LLM to generate patches. While the LLM-based APR
tools are able to achieve state-of-the-art results, it still follows the
classic Generate and Validate repair paradigm of first generating lots of
patches and then validating each one afterwards. This not only leads to many
repeated patches that are incorrect but also miss the crucial information in
test failures as well as in plausible patches.
To address these limitations, we propose ChatRepair, the first fully
automated conversation-driven APR approach that interleaves patch generation
with instant feedback to perform APR in a conversational style. ChatRepair
first feeds the LLM with relevant test failure information to start with, and
then learns from both failures and successes of earlier patching attempts of
the same bug for more powerful APR. For earlier patches that failed to pass all
tests, we combine the incorrect patches with their corresponding relevant test
failure information to construct a new prompt for the LLM to generate the next
patch. In this way, we can avoid making the same mistakes. For earlier patches
that passed all the tests, we further ask the LLM to generate alternative
variations of the original plausible patches. In this way, we can further build
on and learn from earlier successes to generate more plausible patches to
increase the chance of having correct patches. While our approach is general,
we implement ChatRepair using state-of-the-art dialogue-based LLM -- ChatGPT.
By calculating the cost of accessing ChatGPT, we can fix 162 out of 337 bugs
for \$0.42 each!

ChatRepair 是第一个全自动、基于会话的自动程序修复方法，它以对话方式交替进行修补生成和直接反馈以实现自动程序修复。

继续交谈：使用 ChatGPT 每个 0.42 美元修复了 337 个错误中的 162 个

Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each  using ChatGPT

Many of the creative and figurative elements that make language exciting are
lost in translation in current natural language generation engines. In this
paper, we explore a method to harvest templates from positive and negative
reviews in the restaurant domain, with the goal of vastly expanding the types
of stylistic variation available to the natural language generator. We learn
hyperbolic adjective patterns that are representative of the strongly-valenced
expressive language commonly used in either positive or negative reviews. We
then identify and delexicalize entities, and use heuristics to extract
generation templates from review sentences. We evaluate the learned templates
against more traditional review templates, using subjective measures of
"convincingness", "interestingness", and "naturalness". Our results show that
the learned templates score highly on these measures. Finally, we analyze the
linguistic categories that characterize the learned positive and negative
templates. We plan to use the learned templates to improve the conversational
style of dialogue systems in the restaurant domain.

本研究探讨了一种从餐厅领域的正面和负面评论中获取模板的方法，以扩大自然语言生成功能可用的风格变化类型，使用主观评估措施来评估学习的模板，并计划使用学习的模板来改善餐厅领域的对话系统的会话风格。