{"total":76,"rows":[{"id":"2202.03629","title":"Survey of Hallucination in Natural Language Generation","created":"2022-02-08","cited_by_count":2075,"area":null},{"id":"2308.05374","title":"Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment","created":"2023-08-10","cited_by_count":401,"area":null},{"id":"2306.04488","title":"Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards","created":"2023-06-07","cited_by_count":243,"area":"alignment"},{"id":"2307.03987","title":"A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation","created":"2023-07-08","cited_by_count":206,"area":null},{"id":"2206.04624","title":"Factuality Enhanced Language Models for Open-Ended Text Generation","created":"2022-06-09","cited_by_count":202,"area":null},{"id":"2109.09209","title":"CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization","created":"2021-09-19","cited_by_count":120,"area":null},{"id":"2112.08313","title":"Measure and Improve Robustness in NLP Models: A Survey","created":"2021-12-15","cited_by_count":107,"area":null},{"id":"2205.12854","title":"Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors","created":"2022-05-25","cited_by_count":104,"area":null},{"id":"2212.09746","title":"Evaluating Human-Language Model Interaction","created":"2022-12-19","cited_by_count":92,"area":null},{"id":"2306.00946","title":"Exposing Attention Glitches with Flip-Flop Language Modeling","created":"2023-06-01","cited_by_count":75,"area":null},{"id":"2210.13210","title":"Mutual Information Alleviates Hallucinations in Abstractive Summarization","created":"2022-10-24","cited_by_count":68,"area":null},{"id":"2312.01701","title":"Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites","created":"2023-12-04","cited_by_count":61,"area":"mllm"},{"id":"2105.04623","title":"Improving Factual Consistency of Abstractive Summarization via Question Answering","created":"2021-05-10","cited_by_count":55,"area":null},{"id":"2406.11289","title":"A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models","created":"2024-06-17","cited_by_count":52,"area":null},{"id":"2108.13684","title":"Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization","created":"2021-08-31","cited_by_count":51,"area":null},{"id":"2205.07830","title":"FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization","created":"2022-05-16","cited_by_count":49,"area":null},{"id":"2312.10160","title":"Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning","created":"2023-12-15","cited_by_count":49,"area":"mllm"},{"id":"2212.01588","title":"RHO ($\\rho$): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding","created":"2022-12-03","cited_by_count":47,"area":null},{"id":"2204.07447","title":"Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters","created":"2022-04-15","cited_by_count":44,"area":null},{"id":"2407.04121","title":"Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models","created":"2024-07-04","cited_by_count":44,"area":null},{"id":"2410.17146","title":"LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging","created":"2024-10-22","cited_by_count":41,"area":null},{"id":"2212.09968","title":"On Improving Summarization Factual Consistency from Natural Language Feedback","created":"2022-12-20","cited_by_count":38,"area":"alignment"},{"id":"2401.06796","title":"AI Hallucinations: A Misnomer Worth Clarifying","created":"2024-01-09","cited_by_count":34,"area":null},{"id":"2209.03549","title":"Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarization","created":"2022-09-08","cited_by_count":32,"area":null},{"id":"2303.03278","title":"Faithfulness-Aware Decoding Strategies for Abstractive Summarization","created":"2023-03-06","cited_by_count":31,"area":null},{"id":"2212.10750","title":"PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition","created":"2022-12-21","cited_by_count":29,"area":null},{"id":"2309.16155","title":"The Trickle-down Impact of Reward (In-)consistency on RLHF","created":"2023-09-28","cited_by_count":29,"area":"alignment"},{"id":"2204.10290","title":"Learning to Revise References for Faithful Summarization","created":"2022-04-13","cited_by_count":28,"area":null},{"id":"2203.05227","title":"Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods","created":"2022-03-10","cited_by_count":27,"area":null},{"id":"2402.17124","title":"Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models","created":"2024-02-27","cited_by_count":27,"area":null},{"id":"2203.10945","title":"AraBART: a Pretrained Arabic Sequence-to-Sequence Model for Abstractive Summarization","created":"2022-03-21","cited_by_count":24,"area":null},{"id":"2207.02263","title":"Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control","created":"2022-07-05","cited_by_count":21,"area":null},{"id":"2302.05852","title":"\"Why is this misleading?\": Detecting News Headline Hallucinations with Explanations","created":"2023-02-12","cited_by_count":17,"area":null},{"id":"2404.07461","title":"An Audit on the Perspectives and Challenges of Hallucinations in NLP","created":"2024-04-11","cited_by_count":17,"area":null},{"id":"2210.06748","title":"Shortcomings of Question Answering Based Factuality Frameworks for Error Localization","created":"2022-10-13","cited_by_count":15,"area":null},{"id":"2211.06196","title":"Improving Factual Consistency in Summarization with Compression-Based Post-Editing","created":"2022-11-11","cited_by_count":13,"area":null},{"id":"2110.07166","title":"CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization","created":"2021-10-14","cited_by_count":11,"area":null},{"id":"2602.02495","title":"Reward-free Alignment for Conflicting Objectives","created":"2026-02-02","cited_by_count":11,"area":"alignment"},{"id":"2305.16548","title":"Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization","created":"2023-05-26","cited_by_count":10,"area":null},{"id":"2112.01147","title":"CO2Sum:Contrastive Learning for Factual-Consistent Abstractive Summarization","created":"2021-12-02","cited_by_count":9,"area":null},{"id":"2305.17779","title":"Generating EDU Extracts for Plan-Guided Summary Re-Ranking","created":"2023-05-28","cited_by_count":9,"area":null},{"id":"2204.08263","title":"Factual Error Correction for Abstractive Summaries Using Entity Retrieval","created":"2022-04-18","cited_by_count":8,"area":null},{"id":"2205.12467","title":"R2D2: Robust Data-to-Text with Replacement Detection","created":"2022-05-25","cited_by_count":8,"area":null},{"id":"2305.07615","title":"What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization","created":"2023-05-12","cited_by_count":8,"area":null},{"id":"2310.14981","title":"Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation","created":"2023-10-23","cited_by_count":8,"area":null},{"id":"2303.05392","title":"Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges","created":"2023-03-07","cited_by_count":7,"area":null},{"id":"2305.14981","title":"Improving Factuality of Abstractive Summarization without Sacrificing Summary Quality","created":"2023-05-24","cited_by_count":7,"area":null},{"id":"2306.05119","title":"Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework","created":"2023-06-08","cited_by_count":7,"area":null},{"id":"2310.15319","title":"Hallucination Detection for Grounded Instruction Generation","created":"2023-10-23","cited_by_count":7,"area":null},{"id":"2310.16964","title":"Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text Generation","created":"2023-10-25","cited_by_count":7,"area":null},{"id":"2305.04782","title":"HistAlign: Improving Context Dependency in Language Generation by Aligning with History","created":"2023-05-08","cited_by_count":6,"area":null},{"id":"2406.14709","title":"Factual Dialogue Summarization via Learning from Large Language Models","created":"2024-06-20","cited_by_count":6,"area":null},{"id":"2307.00963","title":"Challenges in Domain-Specific Abstractive Summarization and How to Overcome them","created":"2023-07-03","cited_by_count":5,"area":null},{"id":"2310.18794","title":"Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation","created":"2023-10-28","cited_by_count":5,"area":null},{"id":"2211.00294","title":"FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness","created":"2022-11-01","cited_by_count":4,"area":null},{"id":"2310.16176","title":"Correction with Backtracking Reduces Hallucination in Summarization","created":"2023-10-24","cited_by_count":4,"area":null},{"id":"2208.09601","title":"Using Multi-Encoder Fusion Strategies to Improve Personalized Response Selection","created":"2022-08-20","cited_by_count":2,"area":null},{"id":"2307.06524","title":"Agreement Tracking for Multi-Issue Negotiation Dialogues","created":"2023-07-13","cited_by_count":2,"area":null},{"id":"2308.15711","title":"Optimizing Factual Accuracy in Text Generation through Dynamic Knowledge Selection","created":"2023-08-30","cited_by_count":2,"area":null},{"id":"2402.16973","title":"Successfully Guiding Humans with Imperfect Instructions by Highlighting Potential Errors and Suggesting Corrections","created":"2024-02-26","cited_by_count":2,"area":null},{"id":"2403.05788","title":"On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization","created":"2024-03-09","cited_by_count":2,"area":null},{"id":"2305.10736","title":"Counterfactual Debiasing for Generating Factually Consistent Text Summaries","created":"2023-05-18","cited_by_count":1,"area":null},{"id":"2402.15162","title":"Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models","created":"2024-02-23","cited_by_count":1,"area":null},{"id":"2409.17640","title":"T3: A Novel Zero-shot Transfer Learning Framework Iteratively Training on an Assistant Task for a Target Task","created":"2024-09-26","cited_by_count":1,"area":null},{"id":"2411.15661","title":"Improving Next Tokens via Second-to-Last Predictions with Generate and Refine","created":"2024-11-23","cited_by_count":1,"area":null},{"id":"2511.00220","title":"Iterative Foundation Model Fine-Tuning on Multiple Rewards","created":"2025-10-31","cited_by_count":1,"area":null},{"id":"2605.17766","title":"LatentUMM: Dual Latent Alignment for Unified Multimodal Models","created":"2026-05-18","cited_by_count":1,"area":"alignment"},{"id":"2308.12157","title":"Evaluation of Faithfulness Using the Longest Supported Subsequence","created":"2023-08-23","cited_by_count":0,"area":null},{"id":"2312.06022","title":"Exploiting Representation Bias for Data Distillation in Abstractive Text Summarization","created":"2023-12-10","cited_by_count":0,"area":null},{"id":"2402.08581","title":"Improving Factual Error Correction for Abstractive Summarization via Data Distillation and Conditional-generation Cloze","created":"2024-02-13","cited_by_count":0,"area":null},{"id":"2407.15975","title":"Multilingual Fine-Grained News Headline Hallucination Detection","created":"2024-07-22","cited_by_count":0,"area":null},{"id":"2508.06846","title":"Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicators","created":"2025-08-09","cited_by_count":0,"area":null},{"id":"2508.13415","title":"MAVIS: Multi-Objective Alignment via Inference-Time Value-Guided Selection","created":"2025-08-19","cited_by_count":0,"area":"alignment"},{"id":"2510.09915","title":"Enhancing Faithfulness in Abstractive Summarization via Span-Level Fine-Tuning","created":"2025-10-10","cited_by_count":0,"area":null},{"id":"2604.23446","title":"IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance","created":"2026-04-25","cited_by_count":0,"area":null},{"id":"2610.04845","title":"Which Preferences to Train On? End-to-End Multi-Objective Alignment with an Adversarial Preference Distribution","created":"2026-10-04","cited_by_count":0,"area":"alignment"}],"next_cursor":null}