mlsanityData to 14 Oct 2026

Assessing Web Search Credibility and Response Groundedness in Chat Assistants

arXiv 2510.13749 ↗First posted 15 Oct 2025cs.CLcited by 0

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Abstract (arXiv)

Chat assistants increasingly integrate web search functionality, enabling them to retrieve and cite external sources. While this promises more reliable answers, it also raises the risk of amplifying misinformation from low-credibility sources. In this paper, we introduce a novel methodology for evaluating assistants' web search behavior, focusing on source credibility and the groundedness of responses with respect to cited sources. Using 100 claims across five misinformation-prone topics, we assess GPT-4o, GPT-5, Perplexity, and Qwen Chat. Our findings reveal differences between the assistants, with Perplexity achieving the highest source credibility, whereas GPT-4o exhibits elevated citation of non-credibility sources on sensitive topics. This work provides the first systematic comparison of commonly used chat assistants for fact-checking behavior, offering a foundation for evaluating AI systems in high-stakes information environments.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 15 Oct 2025.

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References · 14

  1. 2017Billion-scale similarity search with GPUscited by 2,497
  2. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  3. 2019Defending Against Neural Fake Newscited by 559
  4. 2024mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrievalcited by 236
  5. 2024A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Modelscited by 100
  6. 2023Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkerscited by 87
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  8. 2018Predicting Factuality of Reporting and Bias of News Media Sourcescited by 71
  9. 2020What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Contextcited by 42
  10. 2024FactBench: A Dynamic Benchmark for In-the-Wild Language Model Factuality Evaluationcited by 25
  11. 2023Evaluating Large Language Models for Health-related Queries with Presuppositionscited by 12
  12. 2025LLMs Struggle to Reject False Presuppositions when Misinformation Stakes are Highcited by 12
2 more
  1. 2025News Source Citing Patterns in AI Search Systemscited by 10
  2. 2025Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Expertscited by 3

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