mlsanityData to 14 Oct 2026

CCHall: A Novel Benchmark for Joint Cross-Lingual and Cross-Modal Hallucinations Detection in Large Language Models

arXiv 2505.19108 ↗First posted 25 May 2025cs.CL cs.AIcited by 7

On the map: LLM reasoning → Hallucination detection and mitigation

Abstract (arXiv)

Investigating hallucination issues in large language models (LLMs) within cross-lingual and cross-modal scenarios can greatly advance the large-scale deployment in real-world applications. Nevertheless, the current studies are limited to a single scenario, either cross-lingual or cross-modal, leaving a gap in the exploration of hallucinations in the joint cross-lingual and cross-modal scenarios. Motivated by this, we introduce a novel joint Cross-lingual and Cross-modal Hallucinations benchmark (CCHall) to fill this gap. Specifically, CCHall simultaneously incorporates both cross-lingual and cross-modal hallucination scenarios, which can be used to assess the cross-lingual and cross-modal capabilities of LLMs. Furthermore, we conduct a comprehensive evaluation on CCHall, exploring both mainstream open-source and closed-source LLMs. The experimental results highlight that current LLMs still struggle with CCHall. We hope CCHall can serve as a valuable resource to assess LLMs in joint cross-lingual and cross-modal scenarios.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Hallucination detection and mitigation, one of 17 directions in LLM reasoning. Builds on 34 of its 53 references that are in the map. First posted on arXiv 25 May 2025.

Builds on

34 of 53 references.

References · 34

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  4. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,234
  5. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,332
  6. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
  7. 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suitescited by 1,050
  8. 2018XNLI: Evaluating Cross-lingual Sentence Representationscited by 915
  9. 2018Object Hallucination in Image Captioningcited by 669
  10. 2022AudioLM: a Language Modeling Approach to Audio Generationcited by 616
  11. 2021XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languagescited by 347
  12. 2023AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluationcited by 300
22 more
  1. 2024Multi-Modal Hallucination Control by Visual Information Groundingcited by 208
  2. 2025Multi-agent Architecture Search via Agentic Supernetcited by 179
  3. 2023Hallucinations in Large Multilingual Translation Modelscited by 129
  4. 2025A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deploymentcited by 116
  5. 2024Large Language Models Meet NLP: A Surveycited by 103
  6. 2022Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Datasetcited by 100
  7. 2023Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languagescited by 97
  8. 2025A Survey on Trustworthy LLM Agents: Threats and Countermeasurescited by 96
  9. 2021X-FACT: A New Benchmark Dataset for Multilingual Fact Checkingcited by 91
  10. 2024The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audiocited by 53
  11. 2023Detecting and Mitigating Hallucinations in Multilingual Summarisationcited by 48
  12. 2023HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translationcited by 35
  13. 2024MM-Eval: A Multilingual Meta-Evaluation Benchmark for LLM-as-a-Judge and Reward Modelscited by 35
  14. 2024Understanding Sounds, Missing the Questions: The Challenge of Object Hallucination in Large Audio-Language Modelscited by 31
  15. 2024NativQA: Multilingual Culturally-Aligned Natural Query for LLMscited by 30
  16. 2024AutoCAP: Towards Automatic Cross-lingual Alignment Planning for Zero-shot Chain-of-Thoughtcited by 19
  17. 2024Interpreting and Mitigating Hallucination in MLLMs through Multi-agent Debatecited by 16
  18. 2024Machine Translation Hallucination Detection for Low and High Resource Languages using Large Language Modelscited by 13
  19. 2024Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Informationcited by 10
  20. 2024Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Modelscited by 8
  21. 2024MASSIVE Multilingual Abstract Meaning Representation: A Dataset and Baselines for Hallucination Detectioncited by 4
  22. 2024ANHALTEN: Cross-Lingual Transfer for German Token-Level Reference-Free Hallucination Detectioncited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

  1. 2025CATCH: A Modular Cross-domain Adaptive Template with Hookcited by 6
  2. 2026MoD-DPO: Towards Mitigating Cross-modal Hallucinations in Omni LLMs using Modality Decoupled Preference Optimizationcited by 6
  3. 2026Let's Think with Images Efficiently! An Interleaved-Modal Chain-of-Thought Reasoning Framework with Dynamic and Precise Visual Thoughtscited by 4
  4. 2026Evaluating Cross-Modal Reasoning Ability and Problem Characteristics with Multimodal Item Response Theorycited by 3
  5. 2026Latent Visual Cache for Video Reasoningcited by 3
  6. 2026SinkTrack: Attention Sink based Context Anchoring for Large Language Modelscited by 2
  7. 2025Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reportscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026CrossHallu: Do Hallucination Signals Generalize Across Languages and Domains in Large Language Model's Internals?
  2. 2024Unified Hallucination Detection for Multimodal Large Language Models
  3. 2026OmniHallu: Unified Hallucination Detection for Cross-Modal Comprehension and Generation in Multimodal Large Language Models
  4. 2024AVHBench: A Cross-Modal Hallucination Benchmark for Audio-Visual Large Language Models
  5. 2024Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations
  6. 2025A Survey of Multimodal Hallucination Evaluation and Detection
  7. 2025HalluSearch at SemEval-2025 Task 3: A Search-Enhanced RAG Pipeline for Hallucination Detection
  8. 2026HalluAudio: A Comprehensive Benchmark for Hallucination Detection in Large Audio-Language Models