| 21.09 |
University of Oxford |
ACL2022 |
TruthfulQA: Measuring How Models Mimic Human Falsehoods |
Benchmark&Truthfulness |
| 23.07 |
Microsoft Research Asia, Hong Kong University of Science and Technology, University of Science and Technology of China, Tsinghua University, Sony AI |
ResearchSquare |
Defending ChatGPT against Jailbreak Attack via Self-Reminder |
Jailbreak Attack&Self-Reminder&AI Security |
| 23.10 |
University of Zurich |
arxiv |
Lost in Translation -- Multilingual Misinformation and its Evolution |
Misinformation&Multilingual |
| 23.10 |
New York University&Javier Rando |
arxiv |
Personas as a Way to Model Truthfulness in Language Models |
Truthfulness&Truthful Persona |
| 23.11 |
Dialpad Canada Inc |
arxiv |
Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs |
Factuality Assessment |
| 23.11 |
The University of Manchester |
arxiv |
Emotion Detection for Misinformation: A Review |
Survey&Misinformation&Emotions |
| 23.11 |
University of Virginia |
arxiv |
Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics |
Language Illusions |
| 23.11 |
University of Illinois Urbana-Champaign |
arxiv |
Learn to Refuse: Making Large Language Models More Controllable and Reliable through Knowledge Scope Limitation and Refusal Mechanism |
Hallucinations&Refusal Mechanism |
| 23.11 |
University of Washington Bothell |
arxiv |
Creating Trustworthy LLMs: Dealing with Hallucinations in Healthcare AI |
Healthcare&Trustworthiness&Hallucinations |
| 23.11 |
Intuit AI Research |
EMNLP2023 |
SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency |
Hallucination Detection&Trustworthiness |
| 23.11 |
Shanghai Jiao Tong University |
arxiv |
Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformation |
Misinformation&Disinformation&Out-of-Context |
| 23.11 |
Hamad Bin Khalifa University |
arxiv |
ArAIEval Shared Task: Persuasion Techniques and Disinformation Detection in Arabic Text |
Disinformation&Arabic Text |
| 23.11 |
UNC-Chapel Hill |
arxiv |
Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges |
Hallucination&Benchmark&Multimodal |
| 23.11 |
Cornell University |
arxiv |
Adapting Fake News Detection to the Era of Large Language Models |
Fake news detection&Generated News&Misinformation |
| 23.11 |
Harbin Institute of Technology |
arxiv |
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions |
Hallucination&Factual Consistency&Trustworthiness |
| 23.11 |
Korea University, KAIST AI,LG AI Research |
arXiv |
VOLCANO: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision |
Multimodal Models&Hallucination&Self-Feedback |
| 23.11 |
Beijing Jiaotong University, Alibaba Group, Peng Cheng Lab |
arXiv |
AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation |
Multi-modal Large Language Models&Hallucination&Benchmark |
| 23.11 |
LMU Munich; Munich Center of Machine Learning; Google Research |
arXiv |
Hallucination Augmented Recitations for Language Models |
Hallucination&Counterfactual Datasets |
| 23.11 |
Stanford University, UNC Chapel Hill |
arxiv |
Fine-tuning Language Models for Factuality |
Factuality&Reference-Free Truthfulness&Direct Preference Optimization |
| 23.11 |
Corporate Data and Analytics Office (CDAO) |
arxiv |
Hallucination-minimized Data-to-answer Framework for Financial Decision-makers |
Financial Decision Making&Hallucination Minimization |
| 23.11 |
Arizona State University |
arxiv |
Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey |
Knowledge Graphs&Hallucinations&Survey |
| 23.11 |
Kempelen Institute of Intelligent Technologies; Brno University of Technology |
arxiv |
Disinformation Capabilities of Large Language Models |
Disinformation Generation&Safety Filters&Automated Evaluation |
| 23.11 |
UNC-Chapel Hill, University of Washington |
arxiv |
EVER: Mitigating Hallucination in Large Language Models through Real-Time Verification and Rectification |
Hallucination&Real-Time Verification&Rectification |
| 23.11 |
Peking University, WeChat AI, Tencent Inc. |
arXiv |
RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge |
External Counterfactual Knowledge&Benchmarking&Robustness |
| 23.11 |
PolyAI Limited |
arXiv |
Dial BEINFO for Faithfulness: Improving Factuality of Information-Seeking Dialogue via Behavioural Fine-Tuning |
Factuality&Behavioural Fine-Tuning&Hallucination |
| 23.11 |
The Hong Kong University of Science and Technology, University of Illinois Urbana-Champaign |
arxiv |
R-Tuning: Teaching Large Language Models to Refuse Unknown Questions |
Hallucination&Refusal-Aware Instruction Tuning&Knowledge Gap |
| 23.11 |
University of Southern California, University of Pennsylvania, University of California Davis |
arxiv |
Deceiving Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination? |
Hallucinations&Semantic Associations&Benchmark |
| 23.11 |
The Ohio State University, University of California Davis |
arxiv |
How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities |
Trustworthiness&Malicious Demonstrations&Adversarial Attacks |
| 23.11 |
University of Sheffield |
arXiv |
Lighter yet More Faithful: Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization |
Hallucinations&&Language Model Reliability |
| 23.11 |
Institute of Information Engineering Chinese Academy of Sciences, University of Chinese Academy of Sciences |
arxiv |
Can Large Language Models Understand Content and Propagation for Misinformation Detection: An Empirical Study |
Misinformation Detection |
| 23.11 |
Shanghai Jiaotong University, Amazon AWS AI, Westlake University, IGSNRR Chinese Academy of Sciences, China |
arXiv |
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus |
Hallucination Detection&Uncertainty-Based Methods&Factuality Checking |
| 23.11 |
Institute of Software Chinese Academy of Sciences, University of Chinese Academy of Sciences |
arXiv |
Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting |
Hallucinations&Knowledge Graphs&Retrofitting |
| 23.11 |
Applied Research Quantiphi |
arxiv |
Minimizing Factual Inconsistency and Hallucination in Large Language Models |
Factual Inconsistency&Hallucination |
| 23.11 |
Microsoft Research, Georgia Tech |
arxiv |
Calibrated Language Models Must Hallucinate |
Hallucination&Calibration&Statistical Analysis |
| 23.11 |
School of Information Renmin University of China |
arxiv |
UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation |
Hallucination&Evaluation Benchmark |
| 23.11 |
DAMO Academy Alibaba Group, Nanyang Technological University, Hupan Lab |
arxiv |
Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding |
Vision-Language Models&Object Hallucinations |
| 23.11 |
Shanghai AI Laboratory |
arxiv |
Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization |
Multimodal Language Models&Hallucination Problem&Direct Preference Optimization |
| 23.12 |
Singapore Management University, Beijing Forestry University, University of Electronic Science and Technology of China |
MMM 2024 |
Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites |
Vision-language Models&Hallucination&Fine-grained Evaluation |
| 23.12 |
Mila, McGill University |
EMNLP2023(findings) |
Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness |
Knowledge Bases&Dataset&Evaluation Protocol |
| 23.12 |
MIT CSAIL |
arxiv |
Cognitive Dissonance: Why Do Language Model Outputs Disagree with Internal Representations of Truthfulness? |
Truthfulness&Internal Representations |
| 23.12 |
University of Illinois Chicago, Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI), UNC Chapel-Hill |
arxiv |
DELUCIONQA: Detecting Hallucinations in Domain-specific Question Answering |
Hallucination Detection&Domain-specific QA&Retrieval-augmented LLMs |
| 23.12 |
The University of Hong Kong, Beihang University |
AAAI2024 |
Improving Factual Error Correction by Learning to Inject Factual Errors |
Factual Error Correction |
| 23.12 |
Allen Institute for AI |
arxiv |
BARDA: A Belief and Reasoning Dataset that Separates Factual Accuracy and Reasoning Ability |
Dataset&Factual Accuracy&Reasoning Ability |
| 23.12 |
Tsinghua University, Shanghai Jiao Tong University, Stanford University, Nanyang Technological University |
arxiv |
The Earth is Flat because...: Investigating LLMs’ Belief towards Misinformation via Persuasive Conversation |
Misinformation&Persuasive Conversation&Factual Questions |
| 23.12 |
University of California Davis |
arXiv |
A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT |
Fake News&Fact-checking |
| 23.12 |
Amazon Web Services |
arxiv |
On Early Detection of Hallucinations in Factual Question Answering |
Hallucinations&Factual Question Answering |
| 23.12 |
University of California Santa Cruz |
arxiv |
Don’t Believe Everything You Read: Enhancing Summarization Interpretability through Automatic Identification of Hallucinations in Large Language Models |
Hallucinations&Faithfulness&Token-level |
| 23.12 |
Department of Radiology, The University of Tokyo Hospital |
arxiv |
Theory of Hallucinations based on Equivariance |
Hallucinations&Equivariance |
| 23.12 |
Georgia Institute of Technology |
arXiv |
REDUCING LLM HALLUCINATIONS USING EPISTEMIC NEURAL NETWORKS |
Hallucinations&Uncertainty Estimation&TruthfulQA |
| 23.12 |
Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Tencent AI Lab |
arXiv |
Alleviating Hallucinations of Large Language Models through Induced Hallucinations |
Hallucinations&Induce-then-Contrast Decoding&Factuality |
| 23.12 |
SKLOIS Institute of Information Engineering Chinese Academy of Sciences, School of Cyber Security University of Chinese Academy of Sciences |
arXiv |
LLM Factoscope: Uncovering LLMs’ Factual Discernment through Inner States Analysis |
Factual Detection&Inner States |
| 24.01 |
The Chinese University of Hong Kong, Tencent AI Lab |
arxiv |
The Earth is Flat? Unveiling Factual Errors in Large Language Models |
Factual Errors&Knowledge Graph&Answer Assessment |
| 24.01 |
NewsBreak, University of Illinois Urbana-Champaign |
arxiv |
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models |
Retrieval-Augmented Generation&Hallucination Detection&Dataset |
| 24.01 |
University of California Berkeley, Université de Montréal, McGill University， Mila |
arxiv |
Uncertainty Resolution in Misinformation Detection |
Misinformation&Uncertainty Resolution |
| 24.01 |
Yale University, Stanford University |
arxiv |
Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models |
Legal Hallucinations |
| 24.01 |
Islamic University of Technology, AI Institute University of South Carolina, Stanford University, Amazon AI |
arxiv |
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models |
ß Hallucination Mitigation |
| 24.01 |
Renmin University of China, Renmin University of China, DIRO, Université de Montréal |
arxiv |
The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models |
Hallucination&Detection and Mitigation&Empirical Study |
| 24.01 |
IIT Hyderabad India, Parmonic USA, University of Glasgow UK, LDRP Institute of Technology and Research India |
arxiv |
Fighting Fire with Fire: Adversarial Prompting to Generate a Misinformation Detection Dataset |
Misinformation Detection&LLM-generated Synthetic Data |
| 24.01 |
University College London |
arxiv |
Hallucination Benchmark in Medical Visual Question Answering |
Medical Visual Question Answering&Hallucination Benchmark |
| 24.01 |
Soochow University |
arxiv |
LightHouse: A Survey of AGI Hallucination |
AGI Hallucination |
| 24.01 |
University of Washington, Carnegie Mellon University, Allen Institute for AI |
arxiv |
Fine-grained Hallucination Detection and Editing for Language Models |
Hallucination Detection&FAVA |
| 24.01 |
Dartmouth College, Université de Montréal, McGill University，Mila |
arxiv |
Comparing GPT-4 and Open-Source Language Models in Misinformation Mitigation |
GPT-4&Misinformation Detection |
| 24.01 |
Utrecht University |
arxiv |
The Pitfalls of Defining Hallucination |
Hallucination |
| 24.01 |
Samsung AI Center |
arxiv |
Hallucination Detection and Hallucination Mitigation: An Investigation |
Hallucination Detection&Hallucination Mitigation |
| 24.01 |
McGill University， Mila, Université de Montréal |
arxiv |
Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation |
Misinformation Mitigation&Uncertainty Quantification&Sample-based Consistency |
| 24.01 |
LY Corporation |
arxiv |
On the Audio Hallucinations in Large Audio-Video Language Models |
Audio Hallucinations&Audio-visual Learning&Audio-video language Models |
| 24.01 |
Sun Yat-sen University Tencent AI Lab |
arXiv |
Mitigating Hallucinations of Large Language Models via Knowledge Consistent Alignment |
Hallucination Mitigation&Knowledge Consistent Alignment |
| 24.01 |
National University of Singapore |
arxiv |
Hallucination is Inevitable: An Innate Limitation of Large Language Models |
Hallucination&Real World LLMs |
| 24.01 |
X2Robot&International Digital Economy Academy |
arXiv |
Learning to Trust Your Feelings: Leveraging Self-awareness in LLMs for Hallucination Mitigation |
Hallucination Mitigation&Knowledge Probing&Reinforcement Learning |
| 24.01 |
University of Texas at Austin, Northeastern University |
arxiv |
Diverse but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation |
Misinformation Detection&Socio-Technical Systems |
| 24.01 |
National University of Defense Technology, National University of Singapore |
arxiv |
SWEA: Changing Factual Knowledge in Large Language Models via Subject Word Embedding Altering |
Factual Knowledge Editing&Word Embeddings |
| 24.02 |
University of Washington, University of California Berkeley, The Hong Kong University of Science and Technology, Carnegie Mellon University |
arxiv |
Don’t Hallucinate Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration |
Knowledge Gaps&Multi-LLM Collaboration |
| 24.02 |
IT Innovation and Research Center, Huawei Technologies |
arxiv |
A Survey on Hallucination in Large Vision-Language Models |
Large Vision-Language Models&Hallucination&Mitigation Strategies |
| 24.02 |
Tianjin University, National University of Singapore, A*STAR |
arxiv |
SKIP \N: A SIMPLE METHOD TO REDUCE HALLUCINATION IN LARGE VISION-LANGUAGE MODELS |
Semantic Shift Bias&Hallucination Mitigation&Vision-Language Models |
| 24.02 |
University of Marburg, University of Mannheim |
EACL Findings 2024 |
The Queen of England is not England’s Queen: On the Lack of Factual Coherency in PLMs |
Factual Coherency&Knowledge Bases |
| 24.02 |
MBZUAI, Monash University, LibrAI, Sofia University |
arxiv |
Factuality of Large Language Models in the Year 2024 |
Factuality&Evaluation&Multimodal LLMs |
| 24.02 |
Institute of Information Engineering, Chinese Academy of Sciences, University of Chinese Academy of Sciences |
arxiv |
Are Large Language Models Table-based Fact-Checkers? |
Table-based Fact Verification&In-context Learning |
| 24.02 |
Zhejiang University, Ant Group |
arxiv |
Unified Hallucination Detection for Multimodal Large Language Models |
Multimodal Large Language Models&Hallucination Detection&Benchmark |
| 24.02 |
Alibaba Cloud, Zhejiang University |
ICLR2024 |
INSIDE: LLMS’ INTERNAL STATES RETAIN THE POWER OF HALLUCINATION DETECTION |
Hallucination Detection&EigenScore |
| 24.02 |
The Hong Kong University of Science and Technology, University of Illinois at Urbana-Champaign, The Hong Kong Polytechnic University |
arxiv |
The Instinctive Bias: Spurious Images lead to Hallucination in MLLMs |
Multimodal Large Language Models&Hallucination |
| 24.02 |
Institute of Automation Chinese Academy of Sciences, University of Chinese Academy of Sciences |
arxiv |
Can Large Language Models Detect Rumors on Social Media? |
Rumor Detection&Social Media |
| 24.02 |
CAS Key Laboratory of AI Safety, School of Computer Science and Technology University of Chinese Academy of Science, International Digital Economy Academy IDEA Research |
arxiv |
A Survey on Large Language Model Hallucination via a Creativity Perspective |
Creativity&Hallucination |
| 24.02 |
University College London, Speechmatics, MATS, Anthropic, FAR AI |
arxiv |
Debating with More Persuasive LLMs Leads to More Truthful Answers |
Debate&Truthfulness |
| 24.02 |
University of Illinois Urbana-Champaign, DAMO Academy Alibaba Group, Northwestern University |
arxiv |
Towards Faithful Explainable Fact-Checking via Multi-Agent Debate |
Fact-checking&Explainability |
| 24.02 |
Rice Universitym, Texas A&M University, Wake Forest University, New Jersey Institute of Technology, Meta Platforms Inc. |
arxiv |
Large Language Models As Faithful Explainers |
Explainability&Fidelity&Optimization |
| 24.02 |
The Hong Kong University of Science and Technology |
arxiv |
Do LLMs Know about Hallucination? An Empirical Investigation of LLM’s Hidden States |
Hallucination&Hidden States&Model Interpretation |
| 24.02 |
UC Santa Cruz, ByteDance Research, Northwestern University |
arxiv |
MEASURING AND REDUCING LLM HALLUCINATION WITHOUT GOLD-STANDARD ANSWERS VIA EXPERTISE-WEIGHTING |
Large Language Models (LLMs)&Hallucination&Factualness Evaluations&FEWL |
| 24.02 |
Paul G. Allen School of Computer Science & Engineering, University of Washington |
arxiv |
Comparing Hallucination Detection Metrics for Multilingual Generation |
Hallucination Detection&Multilingual Generation&Lexical Metrics&Natural Language Inference (NLI) |
| 24.02 |
Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences |
arxiv |
Retrieve Only When It Needs: Adaptive Retrieval Augmentation for Hallucination Mitigation in Large Language Models |
Large Language Models (LLMs)&Hallucination Mitigation&Retrieval Augmentation&Rowen |
| 24.02 |
Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Nanjing University |
arxiv |
Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models |
Object Hallucination&Vision-Language Models (LVLMs) |
| 24.02 |
Institute of Mathematics and Statistics University of São Paulo, Artificial Intelligence Specialist in the Banking Sector |
arxiv |
Hallucinations or Attention Misdirection? The Path to Strategic Value Extraction in Business Using Large Language Models |
Hallucinations&Generative Artificial Intelligence |
| 24.02 |
Stevens Institute of Technology, Peraton Labs |
arxiv |
Can Large Language Models Detect Misinformation in Scientific News Reporting? |
Scientific Reporting&Misinformation&Explainability |
| 24.02 |
Middle East Technical University |
arxiv |
HypoTermQA: Hypothetical Terms Dataset for Benchmarking Hallucination Tendency of LLMs |
Hallucination&Benchmarking Dataset |
| 24.02 |
National University of Singapore |
arxiv |
Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding |
Vision-Language Models&Hallucination&CLIP-Guided Decoding |
| 24.02 |
University of California Los Angeles, Cisco Research |
arxiv |
Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension |
Truthfulness&Local Intrinsic Dimension |
| 24.02 |
Institute of Automation Chinese Academy of Sciences, School of Artificial Intelligence University of Chinese Academy of Sciences, Hunan Normal University |
arxiv |
Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models |
False Premise Hallucinations&Attention Mechanism |
| 24.02 |
Shanghai Artificial Intelligence Laboratory, Renmin University of China, University of Chinese Academy of Sciences, Shanghai Jiao Tong University, The University of Sydney |
arxiv |
Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models |
Trustworthiness Dynamics&Pre-training |
| 24.03 |
École polytechnique fédérale de Lausanne, Carnegie Mellon University, University of Maryland College Park |
arxiv |
"Flex Tape Can’t Fix That": Bias and Misinformation in Edited Language Models |
Model Editing&Demographic Bias&Misinformation |
| 24.03 |
East China Normal University |
arxiv |
DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models |
Dialogue-level Hallucination&Benchmarking&Human-machine Interaction |
| 24.03 |
Peking University |
arxiv |
Evaluating and Mitigating Number Hallucinations in Large Vision-Language Models: A Consistency Perspective |
Number Hallucination&Vision-Language Models&Consistency Training |
| 24.03 |
City University of Hong Kong, National University of Singapore, Shanghai Jiao Tong University, Stanford University, Penn State University, Hong Kong University of Science and Technology |
arxiv |
IN-CONTEXT SHARPNESS AS ALERTS: AN INNER REPRESENTATION PERSPECTIVE FOR HALLUCINATION MITIGATION |
Hallucination&Inner Representation&Entropy |
| 24.03 |
Microsoft |
arxiv |
In Search of Truth: An Interrogation Approach to Hallucination Detection |
Hallucination Detection&Interrogation Technique&Balanced Accuracy |
| 24.03 |
Mohamed bin Zayed University of Artificial Intelligence |
arxiv |
Multimodal Large Language Models to Support Real-World Fact-Checking |
Multimodal Large Language Models&Fact-Checking&Misinformation |
| 24.03 |
KAIST, Microsoft Research Asia |
arxiv |
ERBENCH: AN ENTITY-RELATIONSHIP BASED AUTOMATICALLY VERIFIABLE HALLUCINATION BENCHMARK FOR LARGE LANGUAGE MODELS |
Hallucination&Entity-Relationship Model&Benchmarking |
| 24.03 |
University of Alberta, Platform and Content Group, Tencent |
arxiv |
SIFiD: Reassess Summary Factual Inconsistency Detection with LLM |
Factual Consistency&Summarization |
| 24.03 |
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences |
arxiv |
Truth-Aware Context Selection: Mitigating the Hallucinations of Large Language Models Being Misled by Untruthful Contexts |
Truth Detection&Context Selection |
| 24.03 |
UC Berkeley, Google DeepMind |
arxiv |
Unfamiliar Finetuning Examples Control How Language Models Hallucinate |
Large Language Models&Finetuning&Hallucination Control |
| 24.03 |
University of Alberta, Platform and Content Group, Tencent |
arxiv |
SIFiD: Reassess Summary Factual Inconsistency Detection with LLM |
Factual Consistency&Summarization |
| 24.03 |
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences |
arxiv |
Truth-Aware Context Selection: Mitigating the Hallucinations of Large Language Models Being Misled by Untruthful Contexts |
Truth Detection&Context Selection |
| 24.03 |
UC Berkeley, Google DeepMind |
arxiv |
Unfamiliar Finetuning Examples Control How Language Models Hallucinate |
Large Language Models&Finetuning&Hallucination Control |
| 24.03 |
Google Research, UC San Diego |
COLING 2024 |
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics |
Conversational Systems&Evaluation Methodologies |
| 24.03 |
University of Maryland, University of Antwerp, New York University |
arxiv |
Evaluating LLMs for Gender Disparities in Notable Persons |
Bias&Fairness&Hallucinations |
| 24.03 |
University of Duisburg-Essen |
arxiv |
The Human Factor in Detecting Errors of Large Language Models: A Systematic Literature Review and Future Research Directions |
Hallucination |
| 24.03 |
Wuhan University, Beihang University, The University of Sydney, Nanyang Technological University |
COLING 2024 |
Take Care of Your Prompt Bias! Investigating and Mitigating Prompt Bias in Factual Knowledge Extraction |
Factual Knowledge Extraction&Prompt Bias |
| 24.03 |
Carnegie Mellon University |
arxiv |
Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases |
Retrieval Augmented Generation (RAG)&Private Knowledge-Bases&Hallucinations |
| 24.03 |
Integrated Vision and Language Lab KAIST South Korea |
arxiv |
What if...?: Counterfactual Inception to Mitigate Hallucination Effects in Large Multimodal Models |
Large Multimodal Models&Hallucination |
| 24.03 |
UCAS |
arxiv |
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation |
Multimodal Misinformation Detection&Knowledge Distillation |
| 24.03 |
Seoul National University, Sogang University |
arxiv |
Exploiting Semantic Reconstruction to Mitigate Hallucinations in Vision-Language Models |
Semantic Reconstruction&Vision-Language Models&Hallucination Mitigation |
| 24.03 |
University of Illinois Urbana-Champaign |
arxiv |
Hallucination Detection in Foundation Models for Decision-Making: A Flexible Definition and Review of the State of the Art |
Hallucination Detection&Foundation Models&Decision-Making |
| 24.03 |
Shanghai Jiao Tong University |
arxiv |
Rejection Improves Reliability: Training LLMs to Refuse Unknown Questions Using RL from Knowledge Feedback |
Knowledge Feedback&Reliable Reward Model&Refusal Mechanism |
| 24.03 |
Universität Hamburg, The University of Sydney |
arxiv |
Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding |
Instruction Contrastive Decoding&Large Vision-Language Models&Hallucination Mitigation |
| 24.03 |
AI Institute University of South Carolina, Indian Institute of Technology Kharagpur, Islamic University of Technology, Stanford University, Amazon AI |
arxiv |
“Sorry Come Again?” Prompting – Enhancing Comprehension and Diminishing Hallucination with [PAUSE] -injected Optimal Paraphrasing |
Prompt Engineering&Hallucination Mitigation&[PAUSE] Injection |
| 24.04 |
Beihang University, School of Computer Science and Engineering, School of Software, Shandong University |
arxiv |
Exploring and Evaluating Hallucinations in LLM-Powered Code Generation |
Code Generation&Hallucination |
| 24.03 |
Department of Electronic Engineering, Tsinghua University, Pattern Recognition Center, WeChat AI, Tencent Inc, China |
NAACL 2024 |
On Large Language Models’ Hallucination with Regard to Known Facts |
Hallucination&Inference Dynamics |
| 24.04 |
University of California, Berkeley |
NAACL 2024 |
ALOHa: A New Measure for Hallucination in Captioning Models |
Adversarial Attack&AI-Text Detection |
| 24.04 |
Technical University of Munich, University of Stavanger, University of Alberta |
arxiv |
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics |
Hallucination Detection&State Transition Dynamics&Large Language Models |
| 24.04 |
University of Edinburgh, University College London, Peking University, Together AI |
arxiv |
The Hallucinations Leaderboard – An Open Effort to Measure Hallucinations in Large Language Models |
Hallucination Detection&Benchmarking |
| 24.04 |
IIIT Hyderabad, Purdue University, Northwestern University, Indiana University Indianapolis |
arxiv |
Halu-NLP at SemEval-2024 Task 6: MetaCheckGPT - A Multi-task Hallucination Detection Using LLM Uncertainty and Meta-models |
Hallucination Detection&LLM Uncertainty&Meta-models |
| 24.04 |
ServiceNow |
NAACL 2024 |
Reducing hallucination in structured outputs via Retrieval-Augmented Generation |
Retrieval-Augmented Generation&Structured Outputs&Generative AI |
| 24.04 |
Technion – Israel Institute of Technology, Google Research |
arxiv |
Constructing Benchmarks and Interventions for Combating Hallucinations in LLMs |
Hallucinations&Benchmarks |
| 24.04 |
The University of Texas at Austin, Salesforce AI Research |
arxiv |
MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents |
Fact-Checking&Efficiency |
| 24.04 |
Meta, Technical University of Munich |
arxiv |
Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations |
Safety&Hallucinations&Uncertainty |