Papers with mitigating hallucinations
Joint Multimodal Preference Optimization for Fine-Grained Visual-Textual Alignment (2026.findings-eacl)
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| Challenge: | Recent research has focused on addressing multimodal hallucinations in Large Vision-Language Models (LVLMs) however, these methods lack fine-grained visual contrast mechanisms and rely on single-margin optimization. |
| Approach: | They propose a framework that integrates text-conditioned preference loss with visual ranking-based objective. |
| Outcome: | The proposed framework improves cross-modal alignment and fine-grained visual grounding. |
Better Late Than Never: Model-Agnostic Hallucination Post-Processing Framework Towards Clinical Text Summarization (2024.findings-acl)
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| Challenge: | Existing methods for generating concise and coherent summaries may include unintended text with hallucinations, causing computational costs. |
| Approach: | They propose a model-agnostic framework to post-process medical hallucinations . MEDAL integrates with any medical summarization model, requiring no additional computational overhead . |
| Outcome: | MEDAL can post-process medical hallucinations without additional computational overhead. |
Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations (2025.findings-emnlp)
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Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Multimodal large language models have demonstrated remarkable performance in visual-language tasks, but their authenticity is often compromised by object hallucinations. |
| Approach: | They propose a multi-frequency perturbation method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference. |
| Outcome: | The proposed method significantly mitigates object hallucinations across various model architectures. |
Dynamic Planning for LLM-based Graphical User Interface Automation (2024.findings-emnlp)
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| Challenge: | Existing approaches to planning for GUI tasks are limited due to long historical dialogues. |
| Approach: | They propose a novel approach to dynamic planning based on environmental feedback and execution history to guide action prediction in GUI tasks. |
| Outcome: | The proposed approach surpasses the strong GPT-4V baseline by +12.7% in accuracy. |
ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks (2024.findings-naacl)
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| Challenge: | Existing static benchmarks do not guarantee that models can use the provided evidence for answering, which is essential to avoid hallucination when the required knowledge is new or private. |
| Approach: | They propose to automatically perturb existing static one for dynamic evaluation by using a chatGPT framework and a set of open-domain QA datasets. |
| Outcome: | The proposed framework generates new test cases on two open-domain QA datasets and is human-readable and useful to trigger hallucination in LLMs. |
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation (2026.acl-long)
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| Challenge: | Recent advances in large vision-language models produce hallucinations that compromise output reliability. |
| Approach: | They propose a dual-stage framework for mitigating hallucinations without performance degradation . they propose semantic-aware component disentanglement and interpretable parameter updates . |
| Outcome: | The proposed model reduces hallucinations by 23.4% while maintaining 97.4% of general generative capability. |
DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation (2025.acl-long)
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| Challenge: | Existing methods for generating large language models face limitations in key aspects such as retrieval triggers and contextual scrutiny of retrieval content. |
| Approach: | They propose a dynamic RAG method that uses cognitive detection and contextual retrieval optimization to determine when retrieval is needed and what to retrieve for LLMs. |
| Outcome: | The proposed method achieves superior performance on all tasks, demonstrating the effectiveness of the proposed method. |
Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation (2025.emnlp-main)
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| Challenge: | Recent decoding methods improve the factuality of large language models (LLMs) by refining how the next token is selected during generation. |
| Approach: | They propose a decoding strategy that actively decides when to apply contrasting layers during generation by casting decoding as a sequential decision-making problem. |
| Outcome: | The proposed method surpasses state-of-the-art methods across five benchmarks and mitigates hallucinations in diverse generation scenarios. |
Knowledge Verification to Nip Hallucination in the Bud (2024.emnlp-main)
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| Challenge: | Recent studies have shown that large language models generate responses that sound plausible but contradict factual knowledge, a phenomenon known as hallucination. |
| Approach: | They propose a novel approach to align large language models to evaluate knowledge boundaries based on external knowledge to reduce hallucinations . |
| Outcome: | The proposed approach reduces hallucinations across six benchmarks using foundation LLMs of varying backbones and scales. |
Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have impressive capabilities but face significant challenges from hallucinations, which arise from insufficient knowledge or context. |
| Approach: | They propose a novel two-stage approach for contextual question answering that enhances LLMs’ ability to recognise their knowledge boundaries while the second reinforces instruction adherence through carefully designed causal prompts. |
| Outcome: | The proposed approach significantly reduces incorrect answers in contextual QA and improves models’ faithfulness to parametric knowledge, mitigating hallucinations in general QA tasks. |
The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations (2023.emnlp-main)
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Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit Sheth, Amitava Das
| Challenge: | Recent advances in Large Language Models have generated widespread acclaim, but hallucination has also emerged as a by-product. |
| Approach: | They propose a fine-grained discourse on profiling hallucination based on its degree, orientation, and category . they categorize hallucines into six types: acronym ambiguity, generated golem, virtual voice, geographic erratum, time wrap . |
| Outcome: | The proposed method categorizes hallucination into six types based on their degree, orientation, and category . |
Shallow Focus, Deep Fixes: Enhancing Shallow Layers Vision Attention Sinks to Alleviate Hallucination in LVLMs (2025.emnlp-main)
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Xiaofeng Zhang, Yihao Quan, Chen Shen, Chaochen Gu, Xiaosong Yuan, Shaotian Yan, Jiawei Cao, Hao Cheng, Kaijie Wu, Jieping Ye
| Challenge: | Multimodal large language models (MLLMs) demonstrate excellent abilities for understanding visual information, but the hallucination remains a challenging problem. |
| Approach: | They propose a training-free approach to enhance vision attention sinks to facilitate convergence of the image token attention sink within shallow layers. |
| Outcome: | The proposed approach improves the convergence of the image token attention sink within shallow layers and strengthens the layer’s focus on the image itself. |
Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence (2025.acl-long)
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Jinghan He, Kuan Zhu, Haiyun Guo, Junfeng Fang, Zhenglin Hua, Yuheng Jia, Ming Tang, Tat-Seng Chua, Jinqiao Wang
| Challenge: | Existing methods focus on alignment training or decoding refinements but address symptoms at the generation stage without probing the underlying causes. |
| Approach: | They propose a training-free approach to mitigate hallucination by enhancing the role of vision-aware attention heads. |
| Outcome: | The proposed method achieves superior performance compared to state-of-the-art approaches in mitigating hallucinations while maintaining high efficiency with negligible additional time overhead. |
Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey (2024.naacl-long)
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| Challenge: | Increasing the use of knowledge graphs to augment LLMs has led to hallucinations . large language models (LLMs) are prone to producing hallucinosis due to knowledge gaps . |
| Approach: | They review knowledge graph-based augmentation techniques in large language models to assess their effectiveness and examine their performance. |
| Outcome: | The proposed methods have been evaluated against three groups of LLMs and offer methodological comparisons and performance evaluations. |
Pandora’s Box or Aladdin’s Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models (2025.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) has emerged as a promising approach to address hallucinations in large language models (LLMs). |
| Approach: | They define seven distinct noise types from a linguistic perspective and establish a Noise RAG Benchmark (NoiserBench) they propose to evaluate noise that is beneficial to LLMs and noise that's harmful to LRMs. |
| Outcome: | The proposed framework consists of seven distinct noise types from a linguistic perspective and includes multiple datasets and reasoning tasks. |
Compete to Complete: Co-opetition Adversarial Learning for Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing approaches to reduce hallucination in large language models lack a robust mechanism for generating a generative model. |
| Approach: | They propose a framework that formulates retriever–generator training in RAG as a minimax game. |
| Outcome: | The proposed framework improves retrieval-augmented generation performance on seven benchmark datasets. |
Demystifying Uncertainty in LLMs: Active Calibration between Concepts and Human Evaluations (2026.acl-long)
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| Challenge: | Existing static strategies for mitigating hallucinations do not explicitly model the information gain from interacting with the external environment. |
| Approach: | They propose a calibration-driven interactive learning strategy that selects clarification queries by optimizing calibration error. |
| Outcome: | The proposed method provides theoretical guarantees and empirical gains for reliability. |
From Single to Multi: How LLMs Hallucinate in Multi-Document Summarization (2025.findings-naacl)
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| Challenge: | a recent study investigated hallucinations in multi-document summarization tasks . but, it is unclear how challenges arising from handling multiple documents affect outputs . |
| Approach: | They investigate how hallucinations manifest in large language models when summarizing topic-specific information from a set of documents. |
| Outcome: | The proposed benchmarks show that the models generate more hallucinations than baselines . the results highlight the need for more effective approaches to mitigate hallucinosity in MDS . |
HD-NDEs: Neural Differential Equations for Hallucination Detection in LLMs (2025.acl-long)
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| Challenge: | Hallucination is a significant challenge for large language models, but current methods struggle when non-factual information arises in the early or mid-sequence of outputs, reducing their reliability. |
| Approach: | They propose a method that captures the full dynamics of large language models by using neural differential equations to assess the truthfulness of statements. |
| Outcome: | The proposed method achieves 14% improvement in AUC-ROC on the True-False dataset compared to state-of-the-art methods. |
Regularized Contrastive Decoding with Hard Negative Samples for LLM Hallucination Mitigation (2025.findings-emnlp)
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| Challenge: | Large language models are prone to generate hallucinations, which can undermine their reliability in high-stakes applications. |
| Approach: | They propose a method to capture hallucination signals for mitigating hallucis in large language models by regularizing the model's internal signals to a weaker model . |
| Outcome: | The proposed method achieves better hallucination mitigation performance on four benchmarks. |
Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) driven by In-Context Learning (ICL) have improved performance of text-to-SQL. |
| Approach: | They propose a strategy to mitigate hallucinations in large language models driven by In-Context Learning (ICL) they propose TA-SQL, a text-to-Sql framework that encourages LLMs to take advantage of similar tasks rather than starting from scratch. |
| Outcome: | The proposed framework improves the performance of the GPT-4 model by 21.23% on BIRD dev. |
Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval-Augmented Generation (2026.findings-acl)
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Ekaterina Fadeeva, Aleksandr Rubashevskii, Dzianis Piatrashyn, Roman Vashurin, Shehzaad Dhuliawala, Artem Shelmanov, Timothy Baldwin, Preslav Nakov, Mrinmaya Sachan, Maxim Panov
| Challenge: | Existing approaches to mitigating hallucinations conflate factuality with faithfulness to the retrieved evidence, incorrectly labeling factually correct statements as hallucinos . Existing methods to mitigate hallucinics rely on a lack of training data coverage, input ambiguity, and architectural constraints. |
| Approach: | They propose a method for hallucination detection in Large Language Models enhanced with knowledge retrieval based on faithfulness to the retrieved context. |
| Outcome: | The proposed method outperforms unsupervised UQ baselines, RAG-specific methods, and supervised classifiers across multiple tasks and LLMs. |
LearnerCoMPASS: Intelligent Tutoring System with Dynamic Cognitive Diagnosis and Multi-Model Path Planning (2026.acl-long)
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| Challenge: | Existing adaptive learning systems struggle to achieve deep personalization, dynamic adaptability, and content trustworthiness. |
| Approach: | They propose a framework that integrates large language models into adaptive learning systems . they propose 'cognitive multi-model planning adapted system' to enable deep personalization . |
| Outcome: | The proposed framework outperforms state-of-the-art learning paths and improves trustworthiness. |
Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking (2025.findings-acl)
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| Challenge: | Existing studies have focused on the issue of hallucination in large language models. |
| Approach: | They propose a framework that allows an explicit slow thinking generation process for mitigating hallucinations during inference. |
| Outcome: | The proposed framework outperforms baseline approaches on English and Chinese datasets. |
Mixture of Decoding: An Attention-Inspired Adaptive Decoding Strategy to Mitigate Hallucinations in Large Vision-Language Models (2025.findings-acl)
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| Challenge: | Large Vision-Language Models (LVLMs) have impressive capabilities across visual tasks, yet they remain hindered by the persistent challenge of hallucinations. |
| Approach: | They propose a novel approach that dynamically adapts decoding strategies by evaluating the correctness of the model’s attention on image tokens to distinguish the correct attention. |
| Outcome: | Extensive experiments show that the proposed approach outperforms existing decoding methods across multiple mainstream benchmarks, effectively mitigating hallucinations in LVLMs. |
RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human Feedback (2025.coling-main)
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| Challenge: | Existing methods to mitigate hallucinations generate erroneous or fabricated information. |
| Approach: | They propose a rank-response-based model that annotates pair-reponses and trains alignment algorithms to improve the correspondence between images and text. |
| Outcome: | The proposed model outperforms the DPO method and outperfies existing methods on two MLLMs of different sizes and four widely used benchmarks. |
Uplift-RAG: Uplift-Driven Knowledge Preference Alignment for Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Existing efforts to estimate document utility rely on downstream generation performance, which conflates the influence of external documents with the intrinsic knowledge of the LLM. |
| Approach: | They propose an uplift-based definition of document utility that quantifies each document’s marginal benefit over the LLM’s internal knowledge. |
| Outcome: | The proposed framework improves the performance of the LLM by incorporating external retrieved documents into the model. |
Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation (2025.findings-emnlp)
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| Challenge: | Existing approaches to address hallucinations in large vision-language models require substantial computational cost and time. |
| Approach: | They propose to leverage sparse autoencoders to identify semantic directions closely associated with faithfulness or hallucination, extracting more precise and disentangled hallucinian-related representations. |
| Outcome: | The proposed method outperforms existing decoding approaches while maintaining transferability across different model architectures with negligible additional time overhead. |
FACTCHECKMATE: Preemptively Detecting and Mitigating Hallucinations in LMs (2025.findings-emnlp)
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| Challenge: | Language models (LMs) hallucinate. |
| Approach: | They introduce a classifier that predicts whether LMs hallucinate based on model’s hidden states before decoding begins. |
| Outcome: | The proposed model preemptively detects hallucinations by learning a classifier that predicts whether the LM will hallucinate . if a hallucinomy is detected, FactCheckmate intervenes by adjusting the model’s hidden states to produce more factual outputs. |
SubDocTrans: Enhancing Document-level Machine Translation with Plug-and-play Multi-granularity Knowledge Augmentation (2025.findings-emnlp)
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| Challenge: | Document translations generated by large language models suffer from poor consistency, weak coherence, and omission errors. |
| Approach: | They propose a document-level machine translation framework that extracts knowledge from documents to produce high-quality translations. |
| Outcome: | The proposed framework improves consistency and coherence, reduces omission errors, and mitigates hallucinations. |
Learning Fine-Grained Grounded Citations for Attributed Large Language Models (2024.findings-acl)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuxuan Gu, Weihong Zhong, Xiachong Feng, Weijiang Yu, Weihua Peng, Duyu Tang, Dandan Tu, Bing Qin
| Challenge: | despite impressive performance, large language models still struggle with hallucinations . current approaches suffer from suboptimal citation quality due to reliance on in-context learning . |
| Approach: | They propose a framework that teaches large language models to generate fine-grained citations. |
| Outcome: | The proposed framework outperforms all baselines on the ALCE benchmark and achieves an average improvement of 14.21% in citation quality. |
Mitigating Hallucination in Multimodal Large Language Model via Hallucination-targeted Direct Preference Optimization (2025.findings-acl)
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| Challenge: | Multimodal Large Language Models (MLLMs) are known to hallucinate, which limits their practical applications. |
| Approach: | They propose a method that uses three types of preference pairs to target hallucinations from their diverse forms and causes. |
| Outcome: | The proposed method surpasses most state-of-the-art methods and shows potential for further improvements. |
Generate, Discriminate, Evolve: Enhancing Context Faithfulness via Fine-Grained Sentence-Level Self-Evolution (2025.findings-acl)
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Kun Li, Tianhua Zhang, Yunxiang Li, Hongyin Luo, Abdalla Mohamed Salama Sayed Moustafa, Xixin Wu, James R. Glass, Helen M. Meng
| Challenge: | Existing methods to improve context faithfulness in large language models are either inadequate or overlook the potential for self-improvement. |
| Approach: | They propose a framework that enhances context faithfulness through fine-grained sentence-level optimization. |
| Outcome: | Experiments on ASQA and ConFiQA datasets show that GenDiE surpasses baselines in faithfulness and correctness and exhibits robust performance for domain adaptation. |
Semantic Contribution-Aware Adaptive Retrieval for Black-Box Models (2025.findings-emnlp)
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| Challenge: | Existing approaches to retrieval-agmented generation fail to generalize effectively in black-box scenarios. |
| Approach: | They propose a framework that leverages the semantic importance of words to dynamically adjust retrieval thresholds and filter information. |
| Outcome: | The proposed framework achieves the highest score on four long-form, knowledge-intensive generation datasets. |
Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains. |
| Approach: | They propose several strategies for deploying RAG that balance performance and efficiency. |
| Outcome: | The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy. |
No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations. |
| Approach: | They propose a practical three-level threat model from the perspective of user fairness awareness. |
| Outcome: | The proposed model shows that RAG can undermine fairness alignment without fine-tuning or retraining. |
Token Preference Optimization with Self-Calibrated Visual-Anchored Rewards for Hallucination Mitigation (2025.findings-emnlp)
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| Challenge: | Existing methods for predicting hallucinations suffer from two drawbacks: Lack of scalable token-level rewards and Neglect of visual-anchored tokens. |
| Approach: | They propose a Token Preference Optimization model with self-calibrated rewards . they propose based on visual-anchored tokens and visual-aware training objective . |
| Outcome: | The proposed model improves hallucination performance by focusing on visual-anchored tokens without fine-grained annotations. |
Re3: Relevance & Recency Retrieval for Mitigating Temporal Hallucination (2026.acl-long)
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| Challenge: | Existing retrievers suffer from temporal-semantic misalignment and outdated-document interference . Existing frameworks suffer from both temporal validity and outdated factual versions . |
| Approach: | They propose a framework that mitigates temporal hallucinations by embedding heterogeneous temporal signals into the semantic space to ensure retrieval fidelity. |
| Outcome: | Experiments show that Re3 outperforms baselines by 9.7% in generation accuracy . the framework outperformed strongest baselines on challenging dynamic tasks . |
Confidence Estimation for LLMs in Multi-turn Interactions (2026.findings-acl)
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Caiqi Zhang, Ruihan Yang, Xiaochen Zhu, Chengzu Li, Tiancheng Hu, Yijiang River Dong, Deqing Yang, Nigel Collier
| Challenge: | Despite recent progress, most prior work studies confidence in single-turn question answering. |
| Approach: | They propose a logit-based probe that measures confidence in multi-turn dialogues . they propose 'infoECE' and a "hinter-guesser" paradigm for generating controlled evaluations based on data . |
| Outcome: | The proposed framework is grounded in calibration and monotonicity of confidence as more information becomes available. |
Can Hallucination Correction Improve Video-Language Alignment? (2025.findings-acl)
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| Challenge: | Existing work on hallucination correction for large vision-language models focuses on mitigating hallucisations, but a new approach is needed to improve video-language alignment. |
| Approach: | They propose a self-training framework learning to correct hallucinations in descriptions that do not align with the video content. |
| Outcome: | The proposed framework improves video-language alignment by identifying and correcting inconsistencies in descriptions that do not align with the video content. |
Latent Attention Denoising: A Training-Free Energy-Based Framework for Mitigating Hallucinations in Vision-Language Models (2026.acl-long)
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| Challenge: | Existing models for large vision-Language Models lack reliable visual hallucination . despite advances in multimodal perception, they are still limited in real-world applications . |
| Approach: | They propose a framework that recasts attention calibration as a one-step score-based denoising process. |
| Outcome: | The proposed framework achieves superior performance on generative and discriminative tasks while maintaining efficiency comparable to standard decoding. |
PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality (2025.emnlp-main)
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| Challenge: | Existing approaches to decode early exit logits in large language models are lacking in factuality and accuracy. |
| Approach: | They propose a contrastive decoding method that constructs the amateur model via layer pruning rather than early exit. |
| Outcome: | The proposed method improves factuality with minimal inference overhead and is robust and practical. |
DiVE: Decoupling Intra-layer Visual Evidence for Mitigating Hallucinations in Large Vision-Language Models (2026.acl-long)
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| Challenge: | Existing decoding-based approaches do not explicitly decouple visual evidence from mixed vision–language representations. |
| Approach: | They propose to decouple visual evidence from mixed vision–language representations by dynamically identifying layers enriched with visual information and performing intra-layer decoupling to extract aggregated visual evidence. |
| Outcome: | Experiments show that DiVE achieves state-of-the-art performance on multiple benchmarks. |
Med-SRAF: A Multi-Agent Framework for Medical Reasoning via Semantic Routing and Agentic Fusion (2026.findings-acl)
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| Challenge: | Existing RAG methods suffer from a two-part problem: semantic drift and concatenation fallacy . et al.: rapid development of Large Language Models has led to a paradigm shift in artificial intelligence . |
| Approach: | They propose a multi-agent retrieval augmentation framework guided by medical domain knowledge to address these challenges. |
| Outcome: | The proposed framework outperforms existing general RAG baselines on five widely used medical benchmarks. |