Large language models (LLMs) show strong reasoning abilities across diverse tasks, yet their performance on extended contexts remains inconsistent. While prior research has emphasized mid-context degradation in question answering, this study examines the impact of context in LLM-based fact verification. Using three datasets (HOVER, FEVEROUS, and ClimateFEVER) and five open-source models accross different parameters sizes (7B, 32B and 70B parameters) and model families (Llama-3.1, Qwen2.5 and Qwen3), we evaluate both parametric factual knowledge and the impact of evidence placement across varying context lengths. We find that LLMs exhibit non-trivial parametric knowledge of factual claims and that their verification accuracy generally declines as context length increases. Similarly to what has been shown in previous works, in-context evidence placement plays a critical role with accuracy being consistently higher when relevant evidence appears near the beginning or end of the prompt and lower when placed mid-context. These results underscore the importance of prompt structure in retrieval-augmented fact-checking systems.
Towards Comprehensive Vietnamese Retrieval-Augmented Generation and Large Language Models
Foundation Models Lab, BKAI（BKAI基础模型实验室）；Hanoi University of Science and Technology（河内科技大学）；University of Information Technology（信息技术大学）；Vietnam National University HCMC（胡志明市越南国家大学）
TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking
Yam Pak Charitable Foundation School of Computing and Information Sciences, Saint Francis University, Hong Kong（Yam Pak Charitable Foundation 计算与信息科学学院，圣弗朗西斯大学，香港）；Department of Applied Mathematics, Chung Yuan Christian University（应用数学系， Chung Yuan Christian University）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models
Institute for Natural Language Processing, University of Stuttgart（斯图加特大学自然语言处理研究所）；appliedAI Institute for Europe gGmbH（欧洲appliedAI研究所（非营利有限责任公司））；appliedAI Initiative GmbH（appliedAI倡议有限责任公司）
Beyond the Parameters: A Technical Survey of Contextual Enrichment in Large Language Models: From In-Context Prompting to Causal Retrieval-Augmented Generation
Paraphrase generation is a fundamental and long-standing task in natural language processing. In this paper, we concentrate on two contributions to the task: (1) we propose Retrieval Augmented Prompt Tuning (RAPT) as a parameter-efficient method to adapt large pre-trained language models for paraphrase generation; (2) we propose Novelty Conditioned RAPT (NC-RAPT) as a simple model-agnostic method of using specialized prompt tokens for controlled paraphrase generation with varying levels of lexical novelty. By conducting extensive experiments on four datasets, we demonstrate the effectiveness o…
Benchmarking Foundation Models with Retrieval-Augmented Generation in Olympic-Level Physics Problem Solving
AAII, University of Technology Sydney, New South Wales, Australia（AAII，悉尼大学，新南威尔士州，澳大利亚）；Eindhoven University of Technology, Eindhoven, The Netherlands（埃因霍温理工大学，埃因霍温，荷兰）；University of Liverpool, Liverpool, United Kingdom（利物浦大学，利物浦，英国）；University of New South Wales, New South Wales, Australia（新南威尔士大学，新南威尔士州，澳大利亚）
Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Fact-checking has gained increasing attention due to the widespread of falsified information. Most fact-checking approaches focus on claims made in English only due to the data scarcity issue in other languages. The lack of fact-checking datasets in low-resource languages calls for an effective cross-lingual transfer technique for fact-checking. Additionally, trustworthy information in different languages can be complementary and helpful in verifying facts. To this end, we present the first fact-checking framework augmented with cross-lingual retrieval that aggregates evidence retrieved from m…
AfrIFact: Cultural Information Retrieval, Evidence Extraction and Fact Checking for African Languages
Masakhane NLP；Saarland University（萨尔兰大学）；Saarland Informatic Campus（萨尔兰信息学院）；National Institute of Informatics（国家信息研究所）；University of Waterloo（滑铁卢大学）；University College London（伦敦大学学院）；Instituto Politécnico Nacional（国家理工学院）；University of Ilorin（伊洛林大学）；Universidad de Deusto（德尤斯大学）；Bayero University（贝亚罗大学）；University of Lagos（拉各斯大学）；Black Swan（黑天鹅）；Addis Ababa University（亚的斯亚贝巴大学）；Universite Alioune Diop（阿利乌内·迪普大学）；McGill University（麦吉尔大学）
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；University of Cambridge（剑桥大学）；The Chinese University of Hong Kong（香港中文大学）；Tencent AI Lab（腾讯人工智能实验室）
Recently, retrieval-augmented text generation attracted increasing attention of the computational linguistics community. Compared with conventional generation models, retrieval-augmented text generation has remarkable advantages and particularly has achieved state-of-the-art performance in many NLP tasks. This paper aims to conduct a survey about retrieval-augmented text generation. It firstly highlights the generic paradigm of retrieval-augmented generation, and then it reviews notable approaches according to different tasks including dialogue response generation, machine translation, and oth…
CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Beijing Key Laboratory of Artificial Intelligence for Education（北京人工智能教育重点实验室）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Department of Computer Science and Technology, Institute for AI, Tsinghua University（清华大学人工智能研究院计算机科学与技术系）；Northeastern University（东北大学）
Enhancing Retrieval-Augmented LMs with a Two-stage Consistency Learning Compressor
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；Artificial Intelligence Laboratory, China Mobile Communication Group Tianjin Co., Ltd.（中国移动通信集团天津有限公司人工智能实验室）；University College London（伦敦大学学院）
Carnegie Mellon University（卡内基梅隆大学）；Salesforce AI Research（Salesforce人工智能研究院）
Fact-checking is an essential tool to mitigate the spread of misinformation and disinformation. We introduce the task of fact-checking in dialogue, which is a relatively unexplored area. We construct DialFact, a testing benchmark dataset of 22,245 annotated conversational claims, paired with pieces of evidence from Wikipedia. There are three sub-tasks in DialFact: 1) Verifiable claim detection task distinguishes whether a response carries verifiable factual information; 2) Evidence retrieval task retrieves the most relevant Wikipedia snippets as evidence; 3) Claim verification task predicts a …
Columbia University（哥伦比亚大学）；Facebook AI（Facebook人工智能研究院）；George Washington University（乔治华盛顿大学）
The increased focus on misinformation has spurred development of data and systems for detecting the veracity of a claim as well as retrieving authoritative evidence. The Fact Extraction and VERification (FEVER) dataset provides such a resource for evaluating end-to-end fact-checking, requiring retrieval of evidence from Wikipedia to validate a veracity prediction. We show that current systems for FEVER are vulnerable to three categories of realistic challenges for fact-checking -- multiple propositions, temporal reasoning, and ambiguity and lexical variation -- and introduce a resource with th…
DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation
Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University（浙江智能教育技术与应用重点实验室，浙江师范大学）；School of Computer Science and Technology, Zhejiang Normal University（浙江师范大学计算机科学与技术学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；School of Computer Science and Technology, Soochow University（苏州大学计算机科学与技术学院）
