HART: Data-Driven Hallucination Attribution and Evidence-Based Tracing for Large Language Models

• URL: http://arxiv.org/abs/2603.05828v1
• Date: Fri, 06 Mar 2026 02:23:55 GMT
• Title: HART: Data-Driven Hallucination Attribution and Evidence-Based Tracing for Large Language Models
• Abstract summary: We propose HART, a fine-grained hallucination attribution and evidence retrieval framework for large language models.<n>HART formalizes hallucination tracing as a structured modeling task comprising four stages: span localization, mechanism attribution, evidence retrieval, and causal tracing.<n>Based upon this formulation, we develop the first structured dataset tailored for hallucination tracing.
• Score: 4.211691393530721
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have demonstrated remarkable performance in text generation and knowledge-intensive question answering. Nevertheless, they are prone to producing hallucinated content, which severely undermines their reliability in high-stakes application domains. Existing hallucination attribution approaches, based on either external knowledge retrieval or internal model mechanisms, primarily focus on semantic similarity matching or representation-level discrimination. As a result, they have difficulty establishing structured correspondences at the span level between hallucination types, underlying error generation mechanisms, and external factual evidence, thereby limiting the interpretability of hallucinated fragments and the traceability of supporting or opposing evidence. To address these limitations, we propose HART, a fine-grained hallucination attribution and evidence retrieval framework for large language models. HART formalizes hallucination tracing as a structured modeling task comprising four stages: span localization, mechanism attribution, evidence retrieval, and causal tracing. Based upon this formulation, we develop the first structured dataset tailored for hallucination tracing, in which hallucination types, error mechanisms, and sets of counterfactual evidence are jointly annotated to enable causal-level interpretability evaluation. Experimental results on the proposed dataset demonstrate that HART substantially outperforms strong retrieval baselines, including BM25 and DPR, validating the effectiveness and generalization capability of the proposed tracing paradigm for hallucination analysis and evidence alignment.

Related papers

• HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models [70.67112531913669]
  Hallucinations in vision language models are commonly treated as semantic errors.<n>Prior work mainly focuses on detecting or suppressing hallucinations at generation time.<n>We study Post Hallucination Reasoning (PHR), the stage in which hallucinated semantics enter the model's inference context.
  arXiv  Detail & Related papers  (2026-07-08T15:02:11Z)
• DynHD: Hallucination Detection for Diffusion Large Language Models via Denoising Dynamics Deviation Learning [48.77865928715759]
  Diffusion large language models (D-LLMs) have emerged as a promising alternative to auto-regressive models.<n> hallucination remains a critical issue that hinders their reliability.<n>We propose DynHD that bridge these gaps from both spatial (token sequence) and temporal (denoising dynamics) perspectives.
  arXiv  Detail & Related papers  (2026-03-17T12:40:29Z)
• 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 novel hallucination classification framework [0.0]
  This work introduces a novel methodology for the automatic detection of hallucinations generated during large language model (LLM) inference.<n>The proposed approach is based on a systematic taxonomy and controlled reproduction of diverse hallucination types through prompt engineering.
  arXiv  Detail & Related papers  (2025-10-06T09:54:20Z)
• Review of Hallucination Understanding in Large Language and Vision Models [65.29139004945712]
  We present a framework for characterizing both image and text hallucinations across diverse applications.<n>Our investigations reveal that hallucinations often stem from predictable patterns in data distributions and inherited biases.<n>This survey provides a foundation for developing more robust and effective solutions to hallucinations in real-world generative AI systems.
  arXiv  Detail & Related papers  (2025-09-26T09:23:08Z)
• SHALE: A Scalable Benchmark for Fine-grained Hallucination Evaluation in LVLMs [52.03164192840023]
  Large Vision-Language Models (LVLMs) still suffer from hallucinations, i.e., generating content inconsistent with input or established world knowledge.<n>We propose an automated data construction pipeline that produces scalable, controllable, and diverse evaluation data.<n>We construct SHALE, a benchmark designed to assess both faithfulness and factuality hallucinations.
  arXiv  Detail & Related papers  (2025-08-13T07:58:01Z)
• A comprehensive taxonomy of hallucinations in Large Language Models [0.0]
  Large language models (LLMs) have revolutionized natural language processing, yet their propensity for hallucination remains a critical challenge.<n>This report provides a comprehensive taxonomy of LLM hallucinations, beginning with a formal definition and a theoretical framework.<n>It analyzes the underlying causes, categorizing them into data-related issues, model-related factors, and prompt-related influences.
  arXiv  Detail & Related papers  (2025-08-03T14:37:16Z)
• Towards Long Context Hallucination Detection [49.195854802543714]
  Large Language Models (LLMs) have demonstrated remarkable performance across various tasks.<n>They are prone to contextual hallucination, generating information that is either unsubstantiated or contradictory to the given context.<n>We propose a novel architecture that enables pre-trained encoder models, such as BERT, to process long contexts and effectively detect contextual hallucinations.
  arXiv  Detail & Related papers  (2025-04-28T03:47:05Z)
• Why and How LLMs Hallucinate: Connecting the Dots with Subsequence Associations [82.42811602081692]
  This paper introduces a subsequence association framework to systematically trace and understand hallucinations.<n>Key insight is hallucinations that arise when dominant hallucinatory associations outweigh faithful ones.<n>We propose a tracing algorithm that identifies causal subsequences by analyzing hallucination probabilities across randomized input contexts.
  arXiv  Detail & Related papers  (2025-04-17T06:34:45Z)
• Prescribing the Right Remedy: Mitigating Hallucinations in Large Vision-Language Models via Targeted Instruction Tuning [13.805780090705252]
  We propose a targeted instruction data generation framework named DFTG that tailored to the hallucination specificity of different models.<n>The experimental results on hallucination benchmarks demonstrate that the targeted instruction data generated by our method are more effective in mitigating hallucinations compared to previous datasets.
  arXiv  Detail & Related papers  (2024-04-16T07:14:32Z)
• AutoHall: Automated Hallucination Dataset Generation for Large Language Models [56.92068213969036]
  This paper introduces a method for automatically constructing model-specific hallucination datasets based on existing fact-checking datasets called AutoHall. We also propose a zero-resource and black-box hallucination detection method based on self-contradiction.
  arXiv  Detail & Related papers  (2023-09-30T05:20:02Z)

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.