FinTruthQA: A Benchmark for AI-Driven Financial Disclosure Quality Assessment in Investor -- Firm Interactions

[2406.12009] FinTruthQA: A Benchmark for AI-Driven Financial Disclosure Quality Assessment in Investor -- Firm Interactions Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2406.12009 (cs) [Submitted on 17 Jun 2024 ( v1 ), last revised 9 Apr 2026 (this version, v5)] Title: FinTruthQA: A Benchmark for AI-Driven Financial Disclosure Quality Assessment in Investor -- Firm Interactions Authors: Peilin Zhou , Ziyue Xu , Xinyu Shi , Jiageng Wu , Yikang Jiang , Dading Chong , Wang Dong , Jun Chen , Bin Ke , Jie Yang View a PDF of the paper titled FinTruthQA: A Benchmark for AI-Driven Financial Disclosure Quality Assessment in Investor -- Firm Interactions, by Peilin Zhou and 9 other authors View PDF HTML (experimental) Abstract: Accurate and transparent financial information disclosure is essential for market efficiency, investor decision-making, and corporate governance. Chinese stock exchanges' investor interactive platforms provide a widely used channel through which listed firms respond to investor concerns, yet these responses are often limited or non-substantive, making disclosure quality difficult to assess at scale. To address this challenge, we introduce FinTruthQA, to our knowledge the first benchmark for AI-driven assessment of financial disclosure quality in investor-firm interactions. FinTruthQA comprises 6,000 real-world financial Q&A entries, each manually annotated based on four key evaluation criteria: question identification, question relevance, answer readability, and answer relevance. We benchmark statistical machine learning models, pre-trained language models and their fine-tuned variants, as well as large language models (LLMs), on FinTruthQA. Experiments show that existing models achieve strong performance on question identification and question relevance (F1 > 95%), but remain substantially weaker on answer readability (Micro F1 approximately 88%) and especially answer relevance (Micro F1 approximately 80%), highlighting the nontrivial difficulty of fine-grained disclosure quality assessment. Domain- and task-adapted pre-trained language models consistently outperform general-purpose models and LLM-based prompting on the most challenging settings. These findings position FinTruthQA as a practical foundation for AI-driven disclosure monitoring in capital markets, with value for regulatory oversight, investor protection, and disclosure governance in real-world financial settings. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2406.12009 [cs.CL] (or arXiv:2406.12009v5 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2406.12009 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ziyue Xu [ view email ] [v1] Mon, 17 Jun 2024 18:25:02 UTC (1,216 KB) [v2] Sat, 7 Dec 2024 15:47:26 UTC (1,273 KB) [v3] Tue, 11 Feb 2025 16:49:17 UTC (1,042 KB) [v4] Fri, 27 Mar 2026 08:49:53 UTC (778 KB) [v5] Thu, 9 Apr 2026 08:47:41 UTC (778 KB) Full-text links: Access Paper: View a PDF of the paper titled FinTruthQA: A Benchmark for AI-Driven Financial Disclosure Quality Assessment in Investor -- Firm Interactions, by Peilin Zhou and 9 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2024-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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