Detecting Hallucinations in Authentic LLM-Human Interactions

• URL: http://arxiv.org/abs/2510.10539v1
• Date: Sun, 12 Oct 2025 10:46:24 GMT
• Title: Detecting Hallucinations in Authentic LLM-Human Interactions
• Authors: Yujie Ren, Niklas Gruhlke, Anne Lauscher,
• Abstract summary: AuthenHallu is the first hallucination detection benchmark built entirely from authentic LLM-human interactions.<n> Statistical analysis shows that hallucinations occur in 31.4% of the query-response pairs in our benchmark.
• Score: 21.9643190742574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As large language models (LLMs) are increasingly applied in sensitive domains such as medicine and law, hallucination detection has become a critical task. Although numerous benchmarks have been proposed to advance research in this area, most of them are artificially constructed--either through deliberate hallucination induction or simulated interactions--rather than derived from genuine LLM-human dialogues. Consequently, these benchmarks fail to fully capture the characteristics of hallucinations that occur in real-world usage. To address this limitation, we introduce AuthenHallu, the first hallucination detection benchmark built entirely from authentic LLM-human interactions. For AuthenHallu, we select and annotate samples from genuine LLM-human dialogues, thereby providing a faithful reflection of how LLMs hallucinate in everyday user interactions. Statistical analysis shows that hallucinations occur in 31.4% of the query-response pairs in our benchmark, and this proportion increases dramatically to 60.0% in challenging domains such as Math & Number Problems. Furthermore, we explore the potential of using vanilla LLMs themselves as hallucination detectors and find that, despite some promise, their current performance remains insufficient in real-world scenarios.

Related papers

• Beyond Facts: Evaluating Intent Hallucination in Large Language Models [13.315302240710164]
  FAITHQA is a novel benchmark for intent hallucination that contains 20,068 problems.<n>We find that intent hallucination is a common issue even for state-of-the-art models.<n>We introduce an automatic LLM generation evaluation metric, CONSTRAINT SCORE, for detecting intent hallucination.
  arXiv  Detail & Related papers  (2025-06-06T21:10:55Z)
• 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"<n>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)
• WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries [64.239202960816]
  We introduce WildHallucinations, a benchmark that evaluates factuality. It does so by prompting large language models to generate information about entities mined from user-chatbot conversations in the wild. We evaluate 118,785 generations from 15 LLMs on 7,919 entities.
  arXiv  Detail & Related papers  (2024-07-24T17:59:05Z)
• 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)
• HalluDial: A Large-Scale Benchmark for Automatic Dialogue-Level Hallucination Evaluation [19.318217051269382]
  Large Language Models (LLMs) have significantly advanced the field of Natural Language Processing (NLP) HalluDial is the first comprehensive large-scale benchmark for automatic dialogue-level hallucination evaluation. The benchmark includes 4,094 dialogues with a total of 146,856 samples.
  arXiv  Detail & Related papers  (2024-06-11T08:56:18Z)
• InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers [12.427232123205671]
  Large Language Models (LLMs) invent answers that sound realistic, yet drift away from factual truth. We present a novel method for detecting hallucinations in large language models, which tackles a critical issue in the adoption of these models in various real-world scenarios. We observe up to 87% hallucinations for Llama-2 in a specific experiment, where our method achieves a Balanced Accuracy of 81%, all without relying on external knowledge.
  arXiv  Detail & Related papers  (2024-03-05T11:50:01Z)
• DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models [26.289847386286446]
  We propose DiaHalu, the first dialogue-level hallucination evaluation benchmark to our knowledge. We integrate the collected topics into system prompts and facilitate a dialogue between two ChatGPT3.5. We manually modify the contents that do not adhere to human language conventions and then have LLMs re-generate, simulating authentic human-machine interaction scenarios.
  arXiv  Detail & Related papers  (2024-03-01T15:38:55Z)
• 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)
• The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models [134.6697160940223]
  hallucination poses great challenge to trustworthy and reliable deployment of large language models. Three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them. This work presents a systematic empirical study on LLM hallucination, focused on the the three aspects of hallucination detection, source and mitigation.
  arXiv  Detail & Related papers  (2024-01-06T12:40:45Z)
• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models [124.90671698586249]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks.<n>LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
  arXiv  Detail & Related papers  (2023-09-03T16:56:48Z)

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.