Halu-J: Critique-Based Hallucination Judge

• URL: http://arxiv.org/abs/2407.12943v1
• Date: Wed, 17 Jul 2024 18:21:01 GMT
• Title: Halu-J: Critique-Based Hallucination Judge
• Authors: Binjie Wang, Steffi Chern, Ethan Chern, Pengfei Liu,
• Abstract summary: We introduce Halu-J, a critique-based hallucination judge with 7 billion parameters. Halu-J enhances hallucination detection by selecting pertinent evidence and providing detailed critiques. Our experiments indicate that Halu-J outperforms GPT-4o in multiple-evidence hallucination detection.
• Score: 22.79828961875684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) frequently generate non-factual content, known as hallucinations. Existing retrieval-augmented-based hallucination detection approaches typically address this by framing it as a classification task, evaluating hallucinations based on their consistency with retrieved evidence. However, this approach usually lacks detailed explanations for these evaluations and does not assess the reliability of these explanations. Furthermore, deficiencies in retrieval systems can lead to irrelevant or partially relevant evidence retrieval, impairing the detection process. Moreover, while real-world hallucination detection requires analyzing multiple pieces of evidence, current systems usually treat all evidence uniformly without considering its relevance to the content. To address these challenges, we introduce Halu-J, a critique-based hallucination judge with 7 billion parameters. Halu-J enhances hallucination detection by selecting pertinent evidence and providing detailed critiques. Our experiments indicate that Halu-J outperforms GPT-4o in multiple-evidence hallucination detection and matches its capability in critique generation and evidence selection. We also introduce ME-FEVER, a new dataset designed for multiple-evidence hallucination detection. Our code and dataset can be found in https://github.com/GAIR-NLP/factool .

Related papers

• 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" 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)
• 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. Key insight is hallucinations that arise when dominant hallucinatory associations outweigh faithful ones. 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)
• Can Your Uncertainty Scores Detect Hallucinated Entity? [14.432545893757677]
  We propose a new data set, HalluEntity, which annotates hallucination at the entity level. Based on the dataset, we evaluate uncertainty-based hallucination detection approaches across 17 modern LLMs. Our experimental results show that uncertainty estimation approaches focusing on individual token probabilities tend to over-predict hallucinations.
  arXiv  Detail & Related papers  (2025-02-17T16:01:41Z)
• The HalluRAG Dataset: Detecting Closed-Domain Hallucinations in RAG Applications Using an LLM's Internal States [0.5573267589690007]
  We focus on hallucinations involving information not used in training, which we determine by using recency to ensure the information emerged after a cut-off date. This study investigates these hallucinations by detecting them at sentence level using different internal states of various language models. Our results show that IAVs detect hallucinations as effectively as CEVs and reveal that answerable and unanswerable prompts are encoded differently as separate classifiers.
  arXiv  Detail & Related papers  (2024-12-22T15:08:24Z)
• A Unified Hallucination Mitigation Framework for Large Vision-Language Models [18.595958586621943]
  We present a unified framework, Dentist, for hallucination mitigation. The core step is to first classify the queries, then perform different processes of hallucination mitigation based on the classification result. On MMbench, we achieve a 13.44%/10.2%/15.8% improvement in accuracy on Image Quality.
  arXiv  Detail & Related papers  (2024-09-24T22:36:58Z)
• Hallucination Detection: Robustly Discerning Reliable Answers in Large Language Models [70.19081534515371]
  Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks. They generate unfaithful or inconsistent content that deviates from the input source, leading to severe consequences. We propose a robust discriminator named RelD to effectively detect hallucination in LLMs' generated answers.
  arXiv  Detail & Related papers  (2024-07-04T18:47:42Z)
• Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedback [48.065569871444275]
  We propose detecting and mitigating hallucinations in Large Vision Language Models (LVLMs) via fine-grained AI feedback. We generate a small-size hallucination annotation dataset by proprietary models. Then, we propose a detect-then-rewrite pipeline to automatically construct preference dataset for training hallucination mitigating model.
  arXiv  Detail & Related papers  (2024-04-22T14:46:10Z)
• 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)
• 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)
• DelucionQA: Detecting Hallucinations in Domain-specific Question Answering [22.23664008053246]
  Hallucination is a well-known phenomenon in text generated by large language models (LLMs) We introduce a dataset, DelucionQA, that captures hallucinations made by retrieval-augmented LLMs for a domain-specific QA task. We propose a set of hallucination detection methods to serve as baselines for future works from the research community.
  arXiv  Detail & Related papers  (2023-12-08T17:41:06Z)
• HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data [102.56792377624927]
  hallucinations inherent in machine-generated data remain under-explored. We present a novel hallucination detection and elimination framework, HalluciDoctor, based on the cross-checking paradigm. Our method successfully mitigates 44.6% hallucinations relatively and maintains competitive performance compared to LLaVA.
  arXiv  Detail & Related papers  (2023-11-22T04:52:58Z)
• 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)
• Diving Deep into Modes of Fact Hallucinations in Dialogue Systems [2.8360662552057323]
  Knowledge Graph(KG) grounded conversations often use large pre-trained models and usually suffer from fact hallucination. We build an entity-level hallucination detection system, which would provide fine-grained signals that control fallacious content while generating responses.
  arXiv  Detail & Related papers  (2023-01-11T13:08:57Z)

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.