Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering (2025.coling-main)
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| Challenge: | Hallucination remains a critical challenge in large language models (LLMs) in high-stake domains such as legal question answering. |
| Approach: | They propose a method to mitigate hallucination in legal question answering by using behavior cloning and a novel Hard Sample-aware Direct Preference Optimization. |
| Outcome: | The proposed method improves non-hallucinated Statute Rate, Statute Relevance Rate, Legal Claim Truthfulness, and traditional metrics. |
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| Challenge: | Large language models have shown promise for generative and knowledge-intensive tasks including question-answering (QA) but the practical deployment still faces challenges, notably the issue of “hallucination”, where models generate plausible-sounding but unfaithful or nonsensical information. |
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Mitigating Hallucinated Translations in Large Language Models with Hallucination-focused Preference Optimization (2025.naacl-long)
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| Challenge: | Machine Translation (MT) systems based on fine-tuned large language models (LLMs) are at a higher risk of generating hallucinations, which can severely undermine user’s trust and safety. |
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HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models (2026.acl-long)
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| Challenge: | Existing studies on hallucination focus on text or vision, while few audio-oriented studies are limited in scale, modality coverage, and diagnostic depth. |
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How Much Do LLMs Hallucinate across Languages? On Realistic Multilingual Estimation of LLM Hallucination (2025.emnlp-main)
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| 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. |
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Beyond Facts: Evaluating Intent Hallucination in Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) produce unsatisfactory results when faced with complex queries containing multiple conditions. |
| Approach: | They propose a benchmark for intent hallucination that covers 20,068 problems and an automatic LLM generation evaluation metric for detecting intent hallucinosis. |
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FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs (2025.naacl-short)
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Forrest Sheng Bao, Miaoran Li, Renyi Qu, Ge Luo, Erana Wan, Yujia Tang, Weisi Fan, Manveer Singh Tamber, Suleman Kazi, Vivek Sourabh, Mike Qi, Ruixuan Tu, Chenyu Xu, Matthew Gonzales, Ofer Mendelevitch, Amin Ahmad
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The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models (2024.acl-long)
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| Challenge: | a growing number of researchers are studying the hallucination issue in large language models. |
| Approach: | They propose a hallucination detection benchmark and a method to detect hallucines in LLMs. |
| Outcome: | The proposed method detects hallucinations and mitigates them using different training stages. |
HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models (2023.emnlp-main)
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| Challenge: | Existing large language models (LLMs) are prone to generate hallucinations . a recent study shows that LLMs are able to generate content that conflicts with the source or cannot be verified by factual knowledge. |
| Approach: | They propose a framework to evaluate the performance of large language models (LLMs) they propose to use a sample of generated and human-annotated hallucinated samples to evaluate their performance . |
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DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing benchmarks for hallucination detection are intentionally generated by large language models (LLMs) however, many focus on factuality while ignoring faithfulness. |
| Approach: | They propose a dialogue-level hallucination evaluation benchmark for large language models . they integrate the topic into prompts and facilitate a dialog between two LLMs . |
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HALoGEN: Fantastic LLM Hallucinations and Where to Find Them (2025.acl-long)
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| Challenge: | generative large language models produce hallucinations that are not aligned with world knowledge or input context. |
| Approach: | They propose a hallucination benchmark framework that measures hallucinism in large language models . they evaluate 150,000 generations from 14 language models and find they are riddled with hallucinos . |
| Outcome: | The proposed framework evaluates 150,000 generations from 14 language models. |