@inproceedings{benyoash-etal-2025-secque,
title = "{SECQUE}: A Benchmark for Evaluating Real-World Financial Analysis Capabilities",
author = "BenYoash, Noga and
Brief, Menachem and
Ovadia, Oded and
Shenderovitz, Gil and
Mishaeli, Moshik and
Lemberg, Rachel and
Sheetrit, Eitam",
editor = "Arviv, Ofir and
Clinciu, Miruna and
Dhole, Kaustubh and
Dror, Rotem and
Gehrmann, Sebastian and
Habba, Eliya and
Itzhak, Itay and
Mille, Simon and
Perlitz, Yotam and
Santus, Enrico and
Sedoc, Jo{\~a}o and
Shmueli Scheuer, Michal and
Stanovsky, Gabriel and
Tafjord, Oyvind",
booktitle = "Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM{\texttwosuperior})",
month = jul,
year = "2025",
address = "Vienna, Austria and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.gem-1.16/",
pages = "212--230",
ISBN = "979-8-89176-261-9",
abstract = "We introduce SECQUE, a comprehensive benchmark for evaluating large language models (LLMs) in financial analysis tasks. SECQUE comprises 565 expert-written questions covering SEC filings analysis across four key categories: comparison analysis, ratio calculation, risk assessment, and financial insight generation. To assess model performance, we develop SECQUE-Judge, an evaluation mechanism leveraging multiple LLM-based judges, which demonstrates strong alignment with human evaluations. Additionally, we provide an extensive analysis of various models' performance on our benchmark. By making SECQUE publicly available (https://huggingface.co/datasets/nogabenyoash/SecQue), we aim to facilitate further research and advancements in financial AI."
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<abstract>We introduce SECQUE, a comprehensive benchmark for evaluating large language models (LLMs) in financial analysis tasks. SECQUE comprises 565 expert-written questions covering SEC filings analysis across four key categories: comparison analysis, ratio calculation, risk assessment, and financial insight generation. To assess model performance, we develop SECQUE-Judge, an evaluation mechanism leveraging multiple LLM-based judges, which demonstrates strong alignment with human evaluations. Additionally, we provide an extensive analysis of various models’ performance on our benchmark. By making SECQUE publicly available (https://huggingface.co/datasets/nogabenyoash/SecQue), we aim to facilitate further research and advancements in financial AI.</abstract>
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%0 Conference Proceedings
%T SECQUE: A Benchmark for Evaluating Real-World Financial Analysis Capabilities
%A BenYoash, Noga
%A Brief, Menachem
%A Ovadia, Oded
%A Shenderovitz, Gil
%A Mishaeli, Moshik
%A Lemberg, Rachel
%A Sheetrit, Eitam
%Y Arviv, Ofir
%Y Clinciu, Miruna
%Y Dhole, Kaustubh
%Y Dror, Rotem
%Y Gehrmann, Sebastian
%Y Habba, Eliya
%Y Itzhak, Itay
%Y Mille, Simon
%Y Perlitz, Yotam
%Y Santus, Enrico
%Y Sedoc, João
%Y Shmueli Scheuer, Michal
%Y Stanovsky, Gabriel
%Y Tafjord, Oyvind
%S Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria and virtual meeting
%@ 979-8-89176-261-9
%F benyoash-etal-2025-secque
%X We introduce SECQUE, a comprehensive benchmark for evaluating large language models (LLMs) in financial analysis tasks. SECQUE comprises 565 expert-written questions covering SEC filings analysis across four key categories: comparison analysis, ratio calculation, risk assessment, and financial insight generation. To assess model performance, we develop SECQUE-Judge, an evaluation mechanism leveraging multiple LLM-based judges, which demonstrates strong alignment with human evaluations. Additionally, we provide an extensive analysis of various models’ performance on our benchmark. By making SECQUE publicly available (https://huggingface.co/datasets/nogabenyoash/SecQue), we aim to facilitate further research and advancements in financial AI.
%U https://aclanthology.org/2025.gem-1.16/
%P 212-230
Markdown (Informal)
[SECQUE: A Benchmark for Evaluating Real-World Financial Analysis Capabilities](https://aclanthology.org/2025.gem-1.16/) (BenYoash et al., GEM 2025)
ACL
- Noga BenYoash, Menachem Brief, Oded Ovadia, Gil Shenderovitz, Moshik Mishaeli, Rachel Lemberg, and Eitam Sheetrit. 2025. SECQUE: A Benchmark for Evaluating Real-World Financial Analysis Capabilities. In Proceedings of the Fourth Workshop on Generation, Evaluation and Metrics (GEM²), pages 212–230, Vienna, Austria and virtual meeting. Association for Computational Linguistics.