| 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. |
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Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Yezhaohui Wang, Dawei He, Cheng Peng, Zhonghao Wang, Haiying Deng
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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. |
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Language Models Hallucinate, but May Excel at Fact Verification (2024.naacl-long)
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| Challenge: | Recent advances in large language models (LLMs) have produced non-factual outputs . however, current LLMs suffer from the hallucination issue . |
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ANAH: Analytical Annotation of Hallucinations in Large Language Models (2024.acl-long)
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| Challenge: | a comprehensive and fine-grained measurement of the hallucination is crucial for LLMs' wide applications. |
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HalluLens: LLM Hallucination Benchmark (2025.acl-long)
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Yejin Bang, Ziwei Ji, Alan Schelten, Anthony Hartshorn, Tara Fowler, Cheng Zhang, Nicola Cancedda, Pascale Fung
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InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers (2024.acl-long)
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| Challenge: | Existing methods for detecting hallucinations in large language models are limited due to their high frequency and high accuracy. |
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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
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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. |
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Tutorial Proposal: Hallucination in Large Language Models (2024.lrec-tutorials)
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| Challenge: | Grasping the intricacies of hallucination in LLMs can be daunting, especially for those new to the field. |
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Simple Factuality Probes Detect Hallucinations in Long-Form Natural Language Generation (2025.findings-emnlp)
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| Challenge: | Current approaches to detect hallucination require many samples from the LLM generator . current methods require multiple samples, which is computationally infeasible . |
| Approach: | They propose a simple baseline for detecting hallucinations in long-form LLM generations . they show that LLM hidden states are highly predictive of factuality in long form natural language generation . |
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