mlsanityData to 7 Oct 2026

Investigating Hallucination in Conversations for Low Resource Languages

arXiv 2507.22720 ↗First posted 30 Jul 2025cs.CLcited by 2

Not on the map. Why?

Abstract (arXiv)

Large Language Models (LLMs) have demonstrated remarkable proficiency in generating text that closely resemble human writing. However, they often generate factually incorrect statements, a problem typically referred to as 'hallucination'. Addressing hallucination is crucial for enhancing the reliability and effectiveness of LLMs. While much research has focused on hallucinations in English, our study extends this investigation to conversational data in three languages: Hindi, Farsi, and Mandarin. We offer a comprehensive analysis of a dataset to examine both factual and linguistic errors in these languages for GPT-3.5, GPT-4o, Llama-3.1, Gemma-2.0, DeepSeek-R1 and Qwen-3. We found that LLMs produce very few hallucinated responses in Mandarin but generate a significantly higher number of hallucinations in Hindi and Farsi.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 24 of its 28 references that are in the map. First posted on arXiv 30 Jul 2025.

Builds on

24 of 28 references.

References · 24

  1. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  2. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
  3. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  4. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 787
  5. 2017DailyDialog: A Manually Labelled Multi-turn Dialogue Datasetcited by 649
  6. 2023Large Language Models Are Not Robust Multiple Choice Selectorscited by 454
  7. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 329
  8. 2020FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarizationcited by 292
  9. 2023FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarioscited by 247
  10. 2023A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generationcited by 202
  11. 2023Sources of Hallucination by Large Language Models on Inference Taskscited by 168
  12. 2021The Curious Case of Hallucinations in Neural Machine Translationcited by 161
12 more
  1. 2024Fine-grained Hallucination Detection and Editing for Language Modelscited by 146
  2. 2020Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skillscited by 130
  3. 2020On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translationcited by 127
  4. 2023Hallucinations in Large Multilingual Translation Modelscited by 126
  5. 2023Do Language Models Know When They're Hallucinating References?cited by 115
  6. 2020Improved Natural Language Generation via Loss Truncationcited by 75
  7. 2022Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Bettercited by 75
  8. 2023Debiasing Algorithm through Model Adaptationcited by 24
  9. 2023Understanding the Effect of Model Compression on Social Bias in Large Language Modelscited by 19
  10. 2023Co2PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuningcited by 15
  11. 2023"Im not Racist but...": Discovering Bias in the Internal Knowledge of Large Language Modelscited by 5
  12. 2023Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversiescited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025DL3M: A Vision-to-Language Framework for Expert-Level Medical Reasoning through Deep Learning and Large Language Modelscited by 0
  2. 2026Position: AI Leaderboards Are Underserving the Global South: A Case Study from Indiacited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026HalluScore: Large Language Model Hallucination Question Answering Benchmark
  2. 2025OnionEval: An Unified Evaluation of Fact-conflicting Hallucination for Small-Large Language Models
  3. 2026Evaluating Hallucinations in Domain-Adapted Large Language Models
  4. 2025MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations
  5. 2024LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models
  6. 2024Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models
  7. 2025Theoretical Foundations and Mitigation of Hallucination in Large Language Models
  8. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models