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. |
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| Challenge: | Large Language Models (LLMs) have made significant progress on different language tasks, but they tend to "hallucinate" plausible but factually incorrect answers. |
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| Challenge: | Prior studies have shown that fine-tuning on new knowledge can induce factual hallucinations in large language models (LLMs), leading to incorrect outputs when evaluated on previously known information. |
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| Challenge: | Existing benchmarks for large language models are limited by static and narrow questions, leading to limited coverage and misleading evaluations. |
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Shanghao Li, Jinda Han, Yibo Wang, Yuanjie Zhu, Zihe Song, Langzhou He, Kenan Kamel A Alghythee, Philip S. Yu
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| Challenge: | Existing representations of hallucinations limit the types of errors that can be expressed, so we propose a new representation based on free-form textual descriptions, capturing the full range of possible errors. |
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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 . |
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Che Jiang, Biqing Qi, Xiangyu Hong, Dayuan Fu, Yang Cheng, Fandong Meng, Mo Yu, Bowen Zhou, Jie Zhou
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PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations (2026.acl-long)
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Yuhe Wu, Guangyu Wang, Yuran Chen, Jiatong Zhang, Yutong Zhang, Yujie Chen, Jiaming Shang, Guang Zhang, Zhuang Liu
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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. |
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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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