mlsanityData to 7 Oct 2026

HalluSearch at SemEval-2025 Task 3: A Search-Enhanced RAG Pipeline for Hallucination Detection

arXiv 2504.10168 ↗First posted 14 Apr 2025cs.CL cs.AIcited by 2

On the map: Retrieval-augmented generation → Hallucination detection in RAG

Abstract (arXiv)

In this paper, we present HalluSearch, a multilingual pipeline designed to detect fabricated text spans in Large Language Model (LLM) outputs. Developed as part of Mu-SHROOM, the Multilingual Shared-task on Hallucinations and Related Observable Overgeneration Mistakes, HalluSearch couples retrieval-augmented verification with fine-grained factual splitting to identify and localize hallucinations in fourteen different languages. Empirical evaluations show that HalluSearch performs competitively, placing fourth in both English (within the top ten percent) and Czech. While the system's retrieval-based strategy generally proves robust, it faces challenges in languages with limited online coverage, underscoring the need for further research to ensure consistent hallucination detection across diverse linguistic contexts.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Hallucination detection in RAG, one of 15 directions in Retrieval-augmented generation. Builds on 13 of its 18 references that are in the map. First posted on arXiv 14 Apr 2025.

Builds on

13 of 18 references.

References · 13

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,362
  5. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
  6. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  7. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  8. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 329
  9. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 266
  10. 2019MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claimscited by 169
  11. 2024SemEval-2024 Shared Task 6: SHROOM, a Shared-task on Hallucinations and Related Observable Overgeneration Mistakescited by 24
  12. 2024Redefining "Hallucination" in LLMs: Towards a psychology-informed framework for mitigating misinformationcited by 5
1 more
  1. 2024OPDAI at SemEval-2024 Task 6: Small LLMs can Accelerate Hallucination Detection with Weakly Supervised Datacited by 3

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025SemEval-2025 Task 3: Mu-SHROOM, the Multilingual Shared Task on Hallucinations and Related Observable Overgeneration Mistakescited by 27
  2. 2025InterpDetect: Interpretable Signals for Detecting Hallucinations in Retrieval-Augmented Generationcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2023RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
  2. 2024Unified Hallucination Detection for Multimodal Large Language Models
  3. 2026RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration
  4. 2025REFIND at SemEval-2025 Task 3: Retrieval-Augmented Factuality Hallucination Detection in Large Language Models
  5. 2025A Survey of Multimodal Hallucination Evaluation and Detection
  6. 2025Turk-LettuceDetect: A Hallucination Detection Models for Turkish RAG Applications
  7. 2024Lynx: An Open Source Hallucination Evaluation Model
  8. 2025Probabilistic distances-based hallucination detection in LLMs with RAG