PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations

• URL: http://arxiv.org/abs/2604.16909v2
• Date: Sun, 26 Apr 2026 13:12:33 GMT
• Title: PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations
• Abstract summary: PRISM is a benchmark for hallucinations in large language models (LLMs)<n>It disentangles hallucinations into four dimensions: knowledge missing, knowledge errors, reasoning errors, and instruction-following errors, grounded in three stages of generation (memory, instruction, and reasoning)<n> PRISM contains 9,448 instances across 65 tasks and supports fine-grained, stage-aware diagnostic evaluation.
• Score: 17.40617534611707
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models (LLMs) evolve from conversational assistants into agents capable of handling complex tasks, they are increasingly deployed in high-risk domains. However, existing benchmarks largely rely on mixed queries and posterior evaluation, output-level scoring, which quantifies hallucination severity but offers limited insight into where and why hallucinations arise in the generation pipeline. We therefore reformulate hallucination evaluation as a diagnostic problem and propose PRISM, a controlled benchmark that disentangles hallucinations into four dimensions: knowledge missing, knowledge errors, reasoning errors, and instruction-following errors, grounded in three stages of generation (memory, instruction, and reasoning). PRISM contains 9,448 instances across 65 tasks and supports fine-grained, stage-aware diagnostic evaluation. Evaluating 24 mainstream open-source and proprietary LLMs, we uncover consistent trade-offs across instruction following, memory retrieval, and logical reasoning, showing that mitigation strategies often improve specific dimensions at the expense of others. We hope PRISM provides a framework for understanding the specific mechanisms behind LLMs hallucinations, ultimately accelerating the development of trustworthy large language models.

Related papers

• KnowHal: A Knowledge-Driven Benchmark for Comprehensive Multimodal Hallucination Evaluation [9.739392718456843]
  Knowledge-related failures are often investigated separately, lacking a unified evaluation framework across different hallucination dimensions.<n>We propose KnowHal, a benchmark that explicitly incorporates knowledge hallucination into multimodal hallucination evaluation spanning four dimensions.<n>KnowHal constructs paired positive and negative questions over shared images and entities, enabling controlled comparisons among perceptual errors, knowledge-related errors, and false-premise acceptance.
  arXiv  Detail & Related papers  (2026-08-04T15:03:29Z)
• When Prompts Override Vision: Prompt-Induced Hallucinations in LVLMs [54.411658510110215]
  We propose HalluScope to better understand the extent to which different factors induce hallucinations.<n>HalluVL-DPO is a framework for fine-tuning off-the-shelf LVLMs towards more visually grounded responses.
  arXiv  Detail & Related papers  (2026-04-23T17:54:36Z)
• HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs [20.59139155257836]
  We introduce a unified theoretical framework that decomposes hallucination risk into data-driven and reasoning-driven components.<n>We introduce HalluGuard, an NTK-based score that leverages the induced geometry and captured representations of the NTK to jointly identify data-driven and reasoning-driven hallucinations.
  arXiv  Detail & Related papers  (2026-01-26T18:23:09Z)
• A Systematic Literature Review of Code Hallucinations in LLMs: Characterization, Mitigation Methods, Challenges, and Future Directions for Reliable AI [54.34738767990601]
  As Large Language Models become increasingly integrated into software engineering tasks, understanding and mitigating hallucination in code becomes essential.<n>We provide a systematic review of hallucination phenomena in code-oriented LLMs from four key perspectives.
  arXiv  Detail & Related papers  (2025-11-02T02:58:41Z)
• Large Language Models Hallucination: A Comprehensive Survey [3.8100688074986095]
  Large language models (LLMs) have transformed natural language processing, achieving remarkable performance across diverse tasks.<n>Their impressive fluency often comes at the cost of producing false or fabricated information, a phenomenon known as hallucination.<n>This survey provides a comprehensive review of research on hallucination in LLMs, with a focus on causes, detection, and mitigation.
  arXiv  Detail & Related papers  (2025-10-05T20:26:38Z)
• MIRAGE-Bench: LLM Agent is Hallucinating and Where to Find Them [52.764019220214344]
  Hallucinations pose critical risks for large language model (LLM)-based agents.<n>We present MIRAGE-Bench, the first unified benchmark for eliciting and evaluating hallucinations in interactive environments.
  arXiv  Detail & Related papers  (2025-07-28T17:38:29Z)
• MIRAGE: Assessing Hallucination in Multimodal Reasoning Chains of MLLM [58.2298313720146]
  Multimodal hallucinations are multi-sourced and arise from diverse causes.<n>Existing benchmarks fail to adequately distinguish between perception-induced hallucinations and reasoning-induced hallucinations.
  arXiv  Detail & Related papers  (2025-05-30T05:54:36Z)
• Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning [151.4060202671114]
  multimodal large language models (MLLMs) have shown unprecedented capabilities in advancing vision-language tasks.<n>This paper introduces a novel bottom-up reasoning framework to address hallucinations in MLLMs.<n>Our framework systematically addresses potential issues in both visual and textual inputs by verifying and integrating perception-level information with cognition-level commonsense knowledge.
  arXiv  Detail & Related papers  (2024-12-15T09:10:46Z)
• Drowzee: Metamorphic Testing for Fact-Conflicting Hallucination Detection in Large Language Models [11.138489774712163]
  We propose an innovative approach leveraging logic programming to enhance metamorphic testing for detecting Fact-Conflicting Hallucinations (FCH) Our method generates test cases and detects hallucinations across six different large language models spanning nine domains, revealing rates ranging from 24.7% to 59.8%.
  arXiv  Detail & Related papers  (2024-05-01T17:24:42Z)
• KnowHalu: Hallucination Detection via Multi-Form Knowledge Based Factual Checking [55.2155025063668]
  KnowHalu is a novel approach for detecting hallucinations in text generated by large language models (LLMs) It uses step-wise reasoning, multi-formulation query, multi-form knowledge for factual checking, and fusion-based detection mechanism. Our evaluations demonstrate that KnowHalu significantly outperforms SOTA baselines in detecting hallucinations across diverse tasks.
  arXiv  Detail & Related papers  (2024-04-03T02:52:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.