This paper presents an end-to-end system for fact extraction and verification using textual and tabular evidence, the performance of which we demonstrate on the FEVEROUS dataset. We experiment with both a multi-task learning paradigm to jointly train a graph attention network for both the task of evidence extraction and veracity prediction, as well as a single objective graph model for solely learning veracity prediction and separate evidence extraction. In both instances, we employ a framework for per-cell linearization of tabular evidence, thus allowing us to treat evidence from tables as sequences. The templates we employ for linearizing tables capture the context as well as the content of table data. We furthermore provide a case study to show the interpretability our approach. Our best performing system achieves a FEVEROUS score of 0.23 and 53% label accuracy on the blind test data.
Residualized Similarity for Faithfully Explainable Authorship Verification
Department of Computer Science（计算机科学系）；Department of Linguistics（语言学系）；Institute for Advanced Computational Science（先进计算科学研究院）；Stony Brook University（石溪大学）
Given a natural language statement, how to verify its veracity against a large-scale textual knowledge source like Wikipedia? Most existing neural models make predictions without giving clues about which part of a false claim goes wrong. In this paper, we propose LOREN, an approach for interpretable fact verification. We decompose the verification of the whole claim at phrase-level, where the veracity of the phrases serves as explanations and can be aggregated into the final verdict according to logical rules. The key insight of LOREN is to represent claim phrase veracity as three-valued laten…
Fraunhofer Institute for Secure Information Technology SIT（弗劳恩霍夫安全信息技术研究所）
A central problem that has been researched for many years in the field of digital text forensics is the question whether two documents were written by the same author. Authorship verification (AV) is a research branch in this field that deals with this question. Over the years, research activities in the context of AV have steadily increased, which has led to a variety of approaches trying to solve this problem. Many of these approaches, however, make use of features that are related to or influenced by the topic of the documents. Therefore, it may accidentally happen that their verification r…
Department of Computer Science（计算机科学系）；Department of Linguistics（语言学系）；Institute for Advanced Computational Science（先进计算科学研究所）；Stony Brook University（石溪大学）
Recent studies constructing direct interactions between the claim and each single user response (a comment or a relevant article) to capture evidence have shown remarkable success in interpretable claim verification. Owing to different single responses convey different cognition of individual users (i.e., audiences), the captured evidence belongs to the perspective of individual cognition. However, individuals' cognition of social things is not always able to truly reflect the objective. There may be one-sided or biased semantics in their opinions on a claim. The captured evidence correspondin…
Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering
University of South Carolina（南卡罗来纳大学）；IUT（伊斯兰科技大学）；IIT Gandhinagar（甘地纳格尔印度理工学院）；BIT Mesra（贝拉理工学院梅斯拉校区）；Stanford University（斯坦福大学）；Amazon AI（亚马逊人工智能研究院）
Large Language Models are Better Reasoners with Self-Verification
The Laboratory of Cognition and Decision Intelligence for Complex Systems, IA, CAS（中国科学院自动化研究所复杂系统认知与决策智能实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；College of Electrical and Information Engineering, Hunan University（湖南大学电气与信息工程学院）；Unisound（云知声）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal Verification
School of Computer Science, National Key Laboratory for Multimedia Information Processing, PKU-Anker LLM Lab, Peking University（计算机学院、多媒体信息处理国家重点实验室、PKU-Anker LLM实验室、北京大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong Polytechnic University（香港理工大学）
