Neural predictive models have achieved remarkable performance improvements in various natural language processing tasks. However, most neural predictive models suffer from the lack of explainability of predictions, limiting their practical utility. This paper proposes a neural predictive approach to make a prediction and generate its corresponding explanation simultaneously. It leverages the knowledge entailed in explanations as an additional distillation signal for more efficient learning. We conduct a preliminary study on Chinese medical multiple-choice question answering, English natural language inference, and commonsense question answering tasks. The experimental results show that the proposed approach can generate reasonable explanations for its predictions even with a small-scale training corpus. The proposed method also achieves improved prediction accuracy on three datasets, which indicates that making predictions can benefit from generating the explanation in the decision process.
Diverge to Induce Prompting: Multi-Rationale Induction for Zero-Shot Reasoning
Department of Computer Science and Information Engineering, National Taiwan University, Taiwan（国家台湾大学计算机科学与信息工程系）；Institute of Information Science, Academia Sinica, Taiwan（学术院信息科学研究所）；AI Research Center (AINTU), National Taiwan University, Taiwan（国家台湾大学人工智能研究中心（AINTU））
Why Reasoning Fails to Plan: A Planning-Centric Analysis of Long-Horizon Decision Making in LLM Agents
University of Notre Dame（诺丁汉大学）；Stanford University（斯坦福大学）；Purdue University（普渡大学）；University of Edinburgh（爱丁堡大学）；University of Oxford（牛津大学）；Yale University（耶鲁大学）
Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios
Big Data Integration Research Center, National Institute of Information and Communications Technology (NICT), Tokyo, Japan（大数据整合研究室，日本信息通信技术研究所（NICT），东京）
Making Document-Level Information Extraction Right for the Right Reasons
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Pennsylvania（宾夕法尼亚大学）；Galileo CDS Inc.（Galileo CDS公司）
文档级的信息抽取任务（如槽填充）模型是灵活的：它们可以应用于信息不一定局限于单个句子的设置。例如，放射学报告中诊断的关键特征可能没有在一个地方明确说明，但仍然可以从报告的部分文本中推断出来。然而，这些模型很容易学习标签和不相关信息之间的虚假相关性。这项工作研究了如何确保这些模型从复杂的文本中做出正确的推断，并以可审计的方式做出这些推断：除了正确之外，这些模型是否出于正确的原因而正确？“我们使用特征归因技术在预测-选择-验证框架中进行事后证据提取实验。我们发现，在训练过程中进行少量证据监督的正则化可以大大提高提取证据的质量。我们在两个领域进行评估：脑MRI报告的小规模标记数据集和DocRED的大规模修改版本（Yao et al.，2019年），并表明模型的可扩展性可以提高，而不会损失准确性。
Knowledge-Augmented Large Language Model Agents for Explainable Financial Decision-Making
Boston University（波士顿大学）；Hofstra University（霍夫斯特拉大学）；University of Southern California（南加州大学）；Georgia Institute of Technology（佐治亚理工学院）；Carnegie Mellon University（卡内基梅隆大学）
