keepitsimple at SemEval-2025 Task 3: LLM-Uncertainty based Approach for Multilingual Hallucination Span Detection

• URL: http://arxiv.org/abs/2505.17485v1
• Date: Fri, 23 May 2025 05:25:14 GMT
• Title: keepitsimple at SemEval-2025 Task 3: LLM-Uncertainty based Approach for Multilingual Hallucination Span Detection
• Authors: Saketh Reddy Vemula, Parameswari Krishnamurthy,
• Abstract summary: Identification of hallucination spans in black-box language model generated text is essential for applications in the real world.<n>We present our solution to this problem, which capitalizes on the variability ofally-sampled responses in order to identify hallucinated spans.<n>We measure this divergence through entropy-based analysis, allowing for accurate identification of hallucinated segments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identification of hallucination spans in black-box language model generated text is essential for applications in the real world. A recent attempt at this direction is SemEval-2025 Task 3, Mu-SHROOM-a Multilingual Shared Task on Hallucinations and Related Observable Over-generation Errors. In this work, we present our solution to this problem, which capitalizes on the variability of stochastically-sampled responses in order to identify hallucinated spans. Our hypothesis is that if a language model is certain of a fact, its sampled responses will be uniform, while hallucinated facts will yield different and conflicting results. We measure this divergence through entropy-based analysis, allowing for accurate identification of hallucinated segments. Our method is not dependent on additional training and hence is cost-effective and adaptable. In addition, we conduct extensive hyperparameter tuning and perform error analysis, giving us crucial insights into model behavior.

Related papers

• Counterfactual Probing for Hallucination Detection and Mitigation in Large Language Models [0.0]
  We propose Counterfactual Probing, a novel approach for detecting and mitigating hallucinations in large language models.<n>Our method dynamically generates counterfactual statements that appear plausible but contain subtle factual errors, then evaluates the model's sensitivity to these perturbations.
  arXiv  Detail & Related papers  (2025-08-03T17:29:48Z)
• 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)
• RePPL: Recalibrating Perplexity by Uncertainty in Semantic Propagation and Language Generation for Explainable QA Hallucination Detection [29.344966292751817]
  hallucinations remain a vital obstacle to large language models' trustworthy use.<n>We propose RePPL to recalibrate uncertainty measurement by these two aspects.<n>Our method achieves the best comprehensive detection performance across various QA datasets.
  arXiv  Detail & Related papers  (2025-05-21T11:23:05Z)
• HalluLens: LLM Hallucination Benchmark [49.170128733508335]
  Large language models (LLMs) often generate responses that deviate from user input or training data, a phenomenon known as "hallucination"<n>This paper introduces a comprehensive hallucination benchmark, incorporating both new extrinsic and existing intrinsic evaluation tasks.
  arXiv  Detail & Related papers  (2025-04-24T13:40:27Z)
• HausaNLP at SemEval-2025 Task 3: Towards a Fine-Grained Model-Aware Hallucination Detection [1.8230982862848586]
  We aim to provide a nuanced, model-aware understanding of hallucination occurrences and severity in English.<n>We used natural language inference and fine-tuned a ModernBERT model using a synthetic dataset of 400 samples.<n>Results indicate a moderately positive correlation between the model's confidence scores and the actual presence of hallucinations.
  arXiv  Detail & Related papers  (2025-03-25T13:40:22Z)
• SLPL SHROOM at SemEval2024 Task 06: A comprehensive study on models ability to detect hallucination [1.4705596514165422]
  This study explores methods for detecting hallucinations in three SemEval-2024 Task 6 tasks: Machine Translation, Definition Modeling, and Paraphrase Generation. We evaluate two methods: semantic similarity between the generated text and factual references, and an ensemble of language models that judge each other's outputs.
  arXiv  Detail & Related papers  (2024-04-07T07:34:49Z)
• On Large Language Models' Hallucination with Regard to Known Facts [74.96789694959894]
  Large language models are successful in answering factoid questions but are also prone to hallucination. We investigate the phenomenon of LLMs possessing correct answer knowledge yet still hallucinating from the perspective of inference dynamics. Our study shed light on understanding the reasons for LLMs' hallucinations on their known facts, and more importantly, on accurately predicting when they are hallucinating.
  arXiv  Detail & Related papers  (2024-03-29T06:48:30Z)
• Fine-grained Hallucination Detection and Editing for Language Models [109.56911670376932]
  Large language models (LMs) are prone to generate factual errors, which are often called hallucinations. We introduce a comprehensive taxonomy of hallucinations and argue that hallucinations manifest in diverse forms. We propose a novel task of automatic fine-grained hallucination detection and construct a new evaluation benchmark, FavaBench.
  arXiv  Detail & Related papers  (2024-01-12T19:02:48Z)
• 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)
• On Hallucination and Predictive Uncertainty in Conditional Language Generation [76.18783678114325]
  Higher predictive uncertainty corresponds to a higher chance of hallucination. Epistemic uncertainty is more indicative of hallucination than aleatoric or total uncertainties. It helps to achieve better results of trading performance in standard metric for less hallucination with the proposed beam search variant.
  arXiv  Detail & Related papers  (2021-03-28T00:32:27Z)

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.