HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild

• URL: http://arxiv.org/abs/2403.04307v3
• Date: Sun, 15 Sep 2024 05:37:56 GMT
• Title: HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild
• Authors: Zhiying Zhu, Yiming Yang, Zhiqing Sun,
• Abstract summary: Hallucinations pose a significant challenge to the reliability of large language models (LLMs) in critical domains. We introduce HaluEval-Wild, the first benchmark specifically designed to evaluate LLM hallucinations in the wild.
• Score: 41.86776426516293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hallucinations pose a significant challenge to the reliability of large language models (LLMs) in critical domains. Recent benchmarks designed to assess LLM hallucinations within conventional NLP tasks, such as knowledge-intensive question answering (QA) and summarization, are insufficient for capturing the complexities of user-LLM interactions in dynamic, real-world settings. To address this gap, we introduce HaluEval-Wild, the first benchmark specifically designed to evaluate LLM hallucinations in the wild. We meticulously collect challenging (adversarially filtered by Alpaca) user queries from ShareGPT, an existing real-world user-LLM interaction datasets, to evaluate the hallucination rates of various LLMs. Upon analyzing the collected queries, we categorize them into five distinct types, which enables a fine-grained analysis of the types of hallucinations LLMs exhibit, and synthesize the reference answers with the powerful GPT-4 model and retrieval-augmented generation (RAG). Our benchmark offers a novel approach towards enhancing our comprehension of and improving LLM reliability in scenarios reflective of real-world interactions. Our benchmark is available at https://github.com/HaluEval-Wild/HaluEval-Wild.

Related papers

• ANAH: Analytical Annotation of Hallucinations in Large Language Models [65.12177400764506]
  We present $textbfANAH$, a dataset that offers $textbfAN$alytical $textbfA$nnotation of hallucinations in Large Language Models. ANAH consists of 12k sentence-level annotations for 4.3k LLM responses covering over 700 topics, constructed by a human-in-the-loop pipeline. Thanks to the fine granularity of the hallucination annotations, we can quantitatively confirm that the hallucinations of LLMs accumulate in the answer and use ANAH to train and evaluate hallucination annotators.
  arXiv  Detail & Related papers  (2024-05-30T17:54:40Z)
• VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models [57.43276586087863]
  Large Vision-Language Models (LVLMs) suffer from hallucination issues, wherein the models generate plausible-sounding but factually incorrect outputs. Existing benchmarks are often limited in scope, focusing mainly on object hallucinations. We introduce a multi-dimensional benchmark covering objects, attributes, and relations, with challenging images selected based on associative biases.
  arXiv  Detail & Related papers  (2024-04-22T04:49:22Z)
• HypoTermQA: Hypothetical Terms Dataset for Benchmarking Hallucination Tendency of LLMs [0.0]
  Hallucinations pose a significant challenge to the reliability and alignment of Large Language Models (LLMs) This paper introduces an automated scalable framework that combines benchmarking LLMs' hallucination tendencies with efficient hallucination detection. The framework is domain-agnostic, allowing the use of any language model for benchmark creation or evaluation in any domain.
  arXiv  Detail & Related papers  (2024-02-25T22:23:37Z)
• RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models [9.465753274663061]
  Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs) This paper presents RAGTruth, a corpus tailored for analyzing word-level hallucinations in various domains.
  arXiv  Detail & Related papers  (2023-12-31T04:43:45Z)
• AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation [58.19101663976327]
  Multi-modal Large Language Models (MLLMs) encounter the significant challenge of hallucinations. evaluating MLLMs' hallucinations is becoming increasingly important in model improvement and practical application deployment. We propose an LLM-free multi-dimensional benchmark AMBER, which can be used to evaluate both generative task and discriminative task.
  arXiv  Detail & Related papers  (2023-11-13T15:25:42Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models [116.01843550398183]
  Large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks. 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.