FELM is a meta benchmark to evaluate factuality evaluation for large language models. The benchmark comprises 847 questions that span five distinct domains: world knowledge, …

2023年10月1日 · To mitigate this issue, we introduce a benchmark for Factuality Evaluation of large Language Models, referred to as felm. In this benchmark, we collect responses …

FELM is a meta benchmark to evaluate factuality evaluation for large language models. The benchmark comprises 847 questions that span five distinct domains: world knowledge, …

FELM is a meta benchmark to evaluate factuality evaluation benchmark for Large Language Models. Assessing factuality of text generated by large language models (LLMs) is an …

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To mitigate this issue, we introduce a benchmark for Factuality Evaluation of large Language Models, referred to as FELM. In this benchmark, we collect responses generated from LLMs …

2023年12月10日 · To mitigate this issue, we introduce a benchmark for Factuality Evaluation of large Language Models, referred to as FELM. In this benchmark, we collect responses …

2023年11月28日 · In this paper, we introduce FELM, a benchmark to evaluate factuality evaluators. We designed FELM on three principles: 1. Ensuring the authenticity of the factual …

2024年10月13日 · 香港科技大学 本次发布的数据集 FELM, FELM数据集是由香港科技大学开发的一个用于评估大型语言模型真实性的基准。 该数据集收集了来自不同领域的响应，并进行了 …