Papers with hallucination rates
Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards (2025.emnlp-industry)
Copied to clipboard
Manveer Singh Tamber, Forrest Sheng Bao, Chenyu Xu, Ge Luo, Suleman Kazi, Minseok Bae, Miaoran Li, Ofer Mendelevitch, Renyi Qu, Jimmy Lin
| Challenge: | Large language models (LLMs) excel in various tasks, but often produce hallucinations . retrieved contexts, misrepresent information, or generate outright contradictions . |
| Approach: | They propose a framework that measures hallucination faithfulness of large language models . they introduce a leaderboard that leverages diverse human-annotated hallucinian examples . |
| Outcome: | The proposed framework improves hallucination evaluations by leveraging human-annotated examples. |
Memorization Inheritance in Sequence-Level Knowledge Distillation for Neural Machine Translation (2025.acl-short)
Copied to clipboard
| Challenge: | Memorization of noisy training data creates unexpected failure modes in neural machine translation models, thus presenting a reliability risk when deploying them in the real world. |
| Approach: | They propose a modification to sequence-level knowledge distillation (SeqKD) that intervenes in SeqKd to reduce memorization and hallucinations. |
| Outcome: | The proposed modification reduces memorization and hallucinations in the student model . |
Large Language Models for Automated Literature Review: An Evaluation of Reference Generation, Abstract Writing, and Review Composition (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are a promising solution to automate literature review writing tasks. |
| Approach: | They propose a framework to automatically evaluate the performance of large language models in three key tasks of literature review writing: reference generation, abstract writing, and literature review composition. |
| Outcome: | The proposed framework assesses the hallucination rates in generated references and measures the semantic coverage and factual consistency of the literature summaries and compositions against human-written counterparts. |
CCL-XCoT: An Efficient Cross-Lingual Knowledge Transfer Method for Mitigating Hallucination Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Multilingual Large Language Models (MLLMs) exhibit strong generalization across languages, yet they remain prone to hallucinations due to training data imbalances. |
| Approach: | They propose a cross-lingual Chain-of-Thought framework that enhances cross-linguistic alignment . the framework guides the model to reason in a high-resource language before generating answers in low-resourced language. |
| Outcome: | The proposed framework reduces hallucination rates by up to 62% and significantly improves factual knowledge transfer across language pairs. |
TEN: Table Explicitization, Neurosymbolically (2026.acl-industry)
Copied to clipboard
| Challenge: | Existing methods for extracting tabular data from semistructured text are error-prone and costly. |
| Approach: | They propose a neurosymbolic approach to extract tabular data from semistructured text . TEN is a triadic feedback loop that iteratively refines table hypotheses . |
| Outcome: | The proposed approach outperforms neural baselines in exact match accuracy and lower hallucination rates. |
ReLoop: “Seeing Twice and Thinking Backwards” via Closed-loop Training to Mitigate Hallucinations in Multimodal understanding (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for hallucination mitigation rely on external verification or post-hoc correction, lacking internal mechanism to validate outputs directly during training. |
| Approach: | They propose a unified closed-loop training framework that encourages multimodal consistency for cross-modal understanding in MLLMs. |
| Outcome: | The proposed framework encourages multimodal consistency for cross-modal understanding in MLLMs. |
What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance (2026.findings-eacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are often treated as defects of the model or its decoding strategy. |
| Approach: | They construct a 22-dimension query feature vector covering clause complexity, lexical rarity, anaphora, negation, answerability, and intention grounding. |
| Outcome: | The proposed model covers clause complexity, lexical rarity, anaphora, negation, answerability, and intention grounding, all known to affect human comprehension. |
AHEAD: Attention Head Energy-Aware Dynamics for Hallucination Mitigation in MLLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to hallucination mitigation ignore heterogeneous behaviors of attention heads . hallucinosity is a critical barrier to multimodal large language models' reliability, authors say . |
| Approach: | They propose a framework that quantifies the energetic properties of each attention head during object generation through two potential networks and dynamically adjusts their contributions at inference time. |
| Outcome: | The proposed framework reduces hallucination rates without fine-tuning the base model while maintaining generation quality. |
Generating Domain-Specific Knowledge Graphs from Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown impressive world knowledge across different benchmarks and domains but their knowledge is inconveniently scattered across their billions of parameters. |
| Approach: | They propose a prompt-based method to extract knowledge solely from LLMs’ parameters to construct domain-specific KGs by a schema-based process. |
| Outcome: | The proposed method generates large domain-specific KGs containing tens of thousands of entities and relations, and then evaluates against Wikidata, an open-source human-created KG. |
Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering (2025.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for integrating Knowledge Graphs with Large Language Models focus on closed-ended tasks, leaving a gap in evaluating performance on more complex, real-world scenarios. |
| Approach: | They propose a benchmark to evaluate LLMs augmented with KGs in open-ended, real-world question answering settings. |
| Outcome: | The proposed benchmark reflects practical complexities through diverse question types and incorporates metrics to quantify both hallucination rates and reasoning improvements in LLM+KG models. |
Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization (2025.emnlp-main)
Copied to clipboard
Jiulong Wu, Zhengliang Shi, Shuaiqiang Wang, Jizhou Huang, Dawei Yin, Lingyong Yan, Min Cao, Min Zhang
| Challenge: | Existing preference alignment methods focus on aligning model responses with human preferences while neglecting image-text modality alignment. |
| Approach: | They propose Entity-centric Multimodal Preference Optimization to improve modality alignment . they use open-source instruction datasets to automatically construct high-quality preference data . |
| Outcome: | The proposed approach reduces hallucination rates by 80.4% on Object HalBench and 52.6% on MM HalBech. |
PAR: Training-Free Positional Perturbation and Attention Recycling for Faithful OCR (2026.acl-long)
Copied to clipboard
| Challenge: | In high-precision tasks, vision language models suffer from Linguistic Priors Hallucination . |
| Approach: | They propose a training-free, inference-time intervention framework to mitigate this by integrating visual encoders with Large Language Model decoders. |
| Outcome: | The proposed framework reduces hallucination rates by 12% in long-context scenarios while maintaining robust generalization on standard benchmarks. |
MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning (2026.findings-acl)
Copied to clipboard
Ruijun Huang, Zhiqiao Kang, Yuxuan Zhu, Junxiong Li, Jiahao Zhao, Minghuan Tan, Feng Jiang, Min Yang
| Challenge: | Large Language Models (LLMs) exhibit severe hallucinations, which undermine reliability of automated scientific document understanding systems. |
| Approach: | They propose a framework for mitigating scientific measurement hallucinations through enhanced reasoning and targeted optimization. |
| Outcome: | The proposed framework significantly reduces hallucination rates and improves overall accuracy on the MeasEval benchmark. |
How Much Do LLMs Hallucinate across Languages? On Realistic Multilingual Estimation of LLM Hallucination (2025.emnlp-main)
Copied to clipboard
| Challenge: | despite LLMs becoming increasingly multilingual, most studies on detecting and quantifying LLM hallucination are English-centric . |
| Approach: | They train a multilingual hallucination detection model and conduct a large-scale study across 30 languages and 6 open-source LLM families. |
| Outcome: | The proposed model is based on an English-centric model and annotates gold data for five high-resource languages. |
MARCH: Multi-Agent Reinforced Check for Hallucination (2026.acl-long)
Copied to clipboard
Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie Hu, Yu Qin, null Erchao.zec, Xiaoxi Jiang, null Guanjunjiang
| Challenge: | Existing methods to detect hallucinations suffer from inherent confirmation bias, where the verifier inadvertently reproduces the errors of the original generation. |
| Approach: | They propose a framework that enforces rigorous factual alignment by leveraging deliberate *information asymmetry* by combining a pipeline of three specialized agents: a Solver, a Proposer, and a Checker. |
| Outcome: | Extensive experiments across hallucination benchmarks demonstrate that MARCH substantially reduces hallucinism rates. |
Reducing Hallucinations in LLMs via Factuality-Aware Preference Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Preference alignment methods can reinforce hallucinations when preference judgments reward fluency and confidence over factual correctness. |
| Approach: | They propose a method that corrects misordered preference pairs and adds a factuality-aware margin to emphasize pairs with clear correctness differences. |
| Outcome: | The proposed method improves factuality and reduces hallucination rates across seven open-weight LLMs. |
Inject to Heal: Alleviating hallucination in LVLMs via Context Embedding Injection (2026.findings-acl)
Copied to clipboard
| Challenge: | a large vision-language model can generate hallucinations inconsistent with visual input . a lightweight method that embeds the last input token as a grounding signal reduces the likelihood of hallucinosity. |
| Approach: | They propose a training-free mitigation strategy that harnesses the hidden state of the last input token as a grounding signal to maintain visual fidelity throughout decoding and curb hallucinations. |
| Outcome: | The proposed method outperforms state-of-the-art methods on CHAIR, AMBER, and MMHal benchmarks. |