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  • HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large . . .
    To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation benchmark for Large Language Models (HaluEval), a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination
  • HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large . . .
    To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation for Large Language Models (HaluEval) benchmark, a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination
  • HaluEval: A Hallucination Evaluation Benchmark for LLMs
    Based on these data, you can evaluate the ability of LLMs to recognize hallucinations and analyze what type of contents topics LLMs tend to hallucinate (or fail to recognize the contained hallucination)
  • 幻象 or 事实 | HaluEval：大语言模型的幻象评估基准 - 知乎
    为了进一步研究大模型幻象的内容类型和大模型生成幻象的原因，本文提出了用于大语言模型幻象评估的基准—— HaluEval。 我们基于现有的数据集，通过自动生成和手动标注的方式构建了大量的幻象数据组成HaluEval的数据集，其中包含特定于问答、对话、文本摘要任务的30000条样本以及普通用户查询的5000条样本。 在本文中，我们详细介绍了HaluEval数据集的构建过程，对构建的数据集进行了内容分析，并初步探索了大模型识别和减少幻象的策略。 HaluEval包含35000条带幻象的样本和对应的正确样本用于大模型幻象的评估。 为了生成幻象数据集，我们设计了自动生成和人工标注两种构建方式。
  • HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large . . .
    A benchmark named HalluQA (Chinese Hallucination Question-Answering) is established to measure the hallucination phenomenon in Chinese large language models and discusses which types of hallucinations should be prioritized for different types of models
  • HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large . . .
    HaluEval, a benchmark for evaluating hallucinations in Large Language Models, uses a ChatGPT-based framework and human annotations to identify and assess the extent of hallucinations, showing that providing external knowledge or reasoning steps improves hallucination recognition
  • HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large . . .
    TL;DR: This paper introduced the HaluEval benchmark, a large collection of generated and human-annotated hallucinated samples for evaluating the performance of large language models (LLMs) in recognizing hallucination
  • [EMNLP2023] HaluEval: A Large-Scale Hallucination . . .
    We introduce HaluEval, a large-scale collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucinations
  • HaluEval： A Large-Scale Hallucination Evaluation . . .
    To use our benchmark, users can run the code in our project repository to conduct the corresponding evaluation and analysis Users can use our provided instructions on their own datasets to evaluate LLMs on hallucinations
  • HaluEval-Wild: Evaluating Hallucinations of Language Models in the. . .
    TL;DR: We introduce HaluEval-Wild, a fine-grained and chanlleging benchmark for evaluating LLM hallucinations in real-world scenarios Hallucinations pose a significant challenge to the reliability of large language models (LLMs) in critical domains





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