FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial
  Long-Form Question Answering
Authors: name: Yitao Long, name: Tiansheng Hu, name: Yilun Zhao, name: Arman Cohan, name: Chen Zhao

Large Language Models (LLMs) frequently hallucinate to long-form questions,
producing plausible yet factually incorrect answers. A common mitigation
strategy is to provide attribution to LLM outputs. However, existing benchmarks
primarily focus on simple attribution that retrieves supporting textual
evidence as references. We argue that in real-world scenarios such as financial
applications, attribution goes beyond reference retrieval. We introduce
FinLFQA, a benchmark designed to evaluate the ability of LLMs to generate
long-form answers to complex financial questions with reliable and nuanced
attributions. FinLFQA evaluates three critical aspects of attribution through
human annotations: (1) supporting evidence extracted from financial reports,
(2) intermediate numerical reasoning steps, and (3) domain-specific financial
knowledge that informs the reasoning process. We further provide an automatic
evaluation framework covering both answer quality and attribution quality.
Through extensive experiments on eight LLMs across multiple
attribution-generation paradigms, we find that fine-grained metrics are
important to distinguish model capabilities, that end-to-end generation
achieves comparable performance to post-hoc approaches, and that iterative
refinement only helps when guided by external feedback.