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
| Challenge: | Existing benchmarks for hallucination evaluation rely on mixed queries and posterior evaluation, which quantifies hallucinosity severity but offers limited insight into where and why they occur. |
| Approach: | They propose a controlled benchmark that disentangles hallucinations into four dimensions: knowledge missing, knowledge errors, reasoning errors, and instruction-following errors. |
| Outcome: | The proposed model disentangles hallucinations into four dimensions: knowledge missing, knowledge errors, reasoning errors, and instruction-following errors. |
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| Challenge: | generative large language models produce hallucinations that are not aligned with world knowledge or input context. |
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| Challenge: | Large language models generate coherent text and follow instructions across diverse tasks, but a critical challenge in scaling LLM applications is hallucination, where the generated content lacks factual grounding or deviates from the intended discourse context. |
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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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| Challenge: | Large Language Models (LLMs) are often treated as defects of the model or its decoding strategy. |
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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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Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit Sheth, Amitava Das
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| Challenge: | Large language models (LLMs) often generate confident yet inaccurate responses, introducing significant risks for deployment in safety-critical domains. |
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Mitigating Hallucination by Integrating Knowledge Graphs into LLM Inference – a Systematic Literature Review (2025.acl-srw)
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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. |
| Approach: | They propose to integrate knowledge graphs (KGs) into LLM inference to reduce hallucinations by searching online and applying a selection process. |
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