The aim of Logic2Text is to generate controllable and faithful texts conditioned on tables and logical forms, which not only requires a deep understanding of the tables and logical forms, but also warrants symbolic reasoning over the tables. State-of-the-art methods based on pre-trained models have achieved remarkable performance on the standard test dataset. However, we question whether these methods really learn how to perform logical reasoning, rather than just relying on the spurious correlations between the headers of the tables and operators of the logical form. To verify this hypothesis, we manually construct a set of counterfactual samples, which modify the original logical forms to generate counterfactual logical forms with rarely co-occurred table headers and logical operators. SOTA methods give much worse results on these counterfactual samples compared with the results on the original test dataset, which verifies our hypothesis. To deal with this problem, we firstly analyze this bias from a causal perspective, based on which we propose two approaches to reduce the model's reliance on the shortcut. The first one incorporates the hierarchical structure of the logical forms into the model. The second one exploits automatically generated counterfactual data for training. Automatic and manual experimental results on the original test dataset and the counterfactual dataset show that our method is effective to alleviate the spurious correlation. Our work points out the weakness of previous methods and takes a further step toward developing Logic2Text models with real logical reasoning ability.
CLOMO: Counterfactual Logical Modification with Large Language Models
City University of Hong Kong（香港城市大学）；Tsinghua University（清华大学）；Tencent AI Lab（腾讯AI实验室）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；MBZUAI（Mohamed bin Zayed 人工智能大学）；DarkMatter AI Research（DarkMatter AI 研究院）
Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing Institute of Technology（北京理工大学海量语言信息处理与云计算应用北京工程研究中心）
Counterfactual Reasoning with Knowledge Graph Embeddings
Faculty of Computer Science, University of Vienna（维也纳大学计算机学院）；UniVie Doctoral School Computer Science, University of Vienna（维也纳大学UniVie计算机博士学院）；Faculty of Philological and Cultural Studies, University of Vienna（维也纳大学语文学与文化学院）
Wait, but Tylenol is Acetaminophen... Investigating and Improving Language Models' Ability to Resist Requests for Misinformation
Mass General Brigham（麻省总医院布莱根医疗系统）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Boston Children’s Hospital（波士顿儿童医院）；Massachusetts Institute of Technology（麻省理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Virginia（弗吉尼亚大学）；Maastricht University（马斯特里赫特大学）
Can Large Language Models Still Explain Themselves? Investigating the Impact of Quantization on Self-Explanations
Quality and Usability Lab, Technische Universität Berlin（柏林技术大学质量与可用性实验室）；University of Copenhagen（哥本哈根大学）；Saarland Informatics Campus（萨尔州信息学校园）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Centre for European Research in Trusted AI (CERTAIN)（可信AI欧洲研究中心）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究院）
Stands to Reason: Investigating the Effect of Reasoning on Idiomaticity Detection
School of Computer Science, University of Sheffield, UK（谢菲尔德大学计算机科学学院）；Healthy Lifespan Institute, University of Sheffield（谢菲尔德大学健康寿命研究所）；Institute of Data Science and Artificial Intelligence, University of Exeter, UK（埃克塞特大学数据科学与人工智能研究所）
