@inproceedings{he-etal-2026-large,
title = "Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents",
author = "He, Ying and
Gu, Zhouhong and
Hu, Zhecheng and
Zhou, Yubo and
Shen, Hao and
Liang, Jiaqing and
Dai, Zhaoqian and
Shuguang, Ma and
Yu, Fei and
Xiao, Yanghua and
Li, Zhixu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1481/",
pages = "29625--29643",
ISBN = "979-8-89176-395-1",
abstract = "Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making. Several studies have shown that Large Language Models (LLMs) perform well in many financial tasks, such as stock price movements and financial analytics. However, a critical task remains unexplored: the ability of LLMs to identify errors in financial documents. In this paper, we introduce **FinED-Bench**, the first publicly Benchmark for Financial Error Detection across three levels of cognitive complexity. FinED-Bench covers nine real-world financial scenarios, and includes over 900 documents reported in 2025 that are unseen by existing language models. We detail the benchmark construction process and evaluate several advanced LLMs (e.g., GPT-4o, Qwen3-14B) on this tasks, which requires both financial domain knowledge and reasoning capabilities. Experimental results show that current LLMs still struggle with this task, especially in high-complexity cases. Besides, supervised fine-tuning can significantly improve the performance of weaker LLMs on this task. Our data and code are available at https://anonymous.4open.science/r/FinED-Bench-406F."
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<abstract>Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making. Several studies have shown that Large Language Models (LLMs) perform well in many financial tasks, such as stock price movements and financial analytics. However, a critical task remains unexplored: the ability of LLMs to identify errors in financial documents. In this paper, we introduce **FinED-Bench**, the first publicly Benchmark for Financial Error Detection across three levels of cognitive complexity. FinED-Bench covers nine real-world financial scenarios, and includes over 900 documents reported in 2025 that are unseen by existing language models. We detail the benchmark construction process and evaluate several advanced LLMs (e.g., GPT-4o, Qwen3-14B) on this tasks, which requires both financial domain knowledge and reasoning capabilities. Experimental results show that current LLMs still struggle with this task, especially in high-complexity cases. Besides, supervised fine-tuning can significantly improve the performance of weaker LLMs on this task. Our data and code are available at https://anonymous.4open.science/r/FinED-Bench-406F.</abstract>
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%0 Conference Proceedings
%T Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents
%A He, Ying
%A Gu, Zhouhong
%A Hu, Zhecheng
%A Zhou, Yubo
%A Shen, Hao
%A Liang, Jiaqing
%A Dai, Zhaoqian
%A Shuguang, Ma
%A Yu, Fei
%A Xiao, Yanghua
%A Li, Zhixu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F he-etal-2026-large
%X Ensuring the accuracy of financial documents is critical for economic analysis, regulatory compliance, and corporate decision-making. Several studies have shown that Large Language Models (LLMs) perform well in many financial tasks, such as stock price movements and financial analytics. However, a critical task remains unexplored: the ability of LLMs to identify errors in financial documents. In this paper, we introduce **FinED-Bench**, the first publicly Benchmark for Financial Error Detection across three levels of cognitive complexity. FinED-Bench covers nine real-world financial scenarios, and includes over 900 documents reported in 2025 that are unseen by existing language models. We detail the benchmark construction process and evaluate several advanced LLMs (e.g., GPT-4o, Qwen3-14B) on this tasks, which requires both financial domain knowledge and reasoning capabilities. Experimental results show that current LLMs still struggle with this task, especially in high-complexity cases. Besides, supervised fine-tuning can significantly improve the performance of weaker LLMs on this task. Our data and code are available at https://anonymous.4open.science/r/FinED-Bench-406F.
%U https://aclanthology.org/2026.findings-acl.1481/
%P 29625-29643
Markdown (Informal)
[Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents](https://aclanthology.org/2026.findings-acl.1481/) (He et al., Findings 2026)
ACL
- Ying He, Zhouhong Gu, Zhecheng Hu, Yubo Zhou, Hao Shen, Jiaqing Liang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao, and Zhixu Li. 2026. Are Large Language Models Reliable Reviewers? A Benchmark for Error Detection in Financial Documents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 29625–29643, San Diego, California, United States. Association for Computational Linguistics.