Modern generative search engines enhance the reliability of large language
model (LLM) responses by providing cited evidence. However, evaluating the
answer's attribution, i.e., whether every claim within the generated responses
is fully supported by its cited evidence, remains an open problem. This
verification, traditionally dependent on costly human evaluation, underscores
the urgent need for automatic attribution evaluation methods. To bridge the gap
in the absence of standardized benchmarks for these methods, we present
AttributionBench, a comprehensive benchmark compiled from various existing
attribution datasets. Our extensive experiments on AttributionBench reveal the
challenges of automatic attribution evaluation, even for state-of-the-art LLMs.
Specifically, our findings show that even a fine-tuned GPT-3.5 only achieves
around 80% macro-F1 under a binary classification formulation. A detailed
analysis of more than 300 error cases indicates that a majority of failures
stem from the model's inability to process nuanced information, and the
discrepancy between the information the model has access to and that human
annotators do.

通过提供引用的证据，现代生成式搜索引擎增强了大型语言模型（LLM）响应的可靠性。为了弥补缺乏这些方法的标准化基准的差距，我们提出了 AttributionBench，这是一个由各种现有归因数据集编制的综合基准。我们在 AttributionBench 上进行了大量实验，揭示了即使对于最先进的 LLM 也存在自动归因评估的挑战。