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---
license: mit
task_categories:
- question-answering
language:
- en
tags:
- finance
- table-text
- numerical_reasoning
size_categories:
- < 1K
---
# SECQUE
- [**Paper**](https://arxiv.org/abs/2504.04596)
SECQUE is 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
- 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.
## Results
| Model | Baseline | Financial | Baseline CoT | Financial CoT | Flipped | Avg Tokens by Model |
|------------------------------|-------------------|------------------|--------------------|--------------------|-------------------|---------------------|
| GPT-4o | **_0.69_**/0.79 | 0.62/0.71 | 0.67/0.76 | 0.63/0.73 | 0.68/0.78 | 319.84 |
| GPT-4o-mini | _0.64_/0.73 | 0.38/0.47 | 0.60/0.72 | 0.56/0.65 | 0.62/0.73 | 289.76 |
| Llama-3.3-70B-Instruct | _0.65_/0.75 | 0.60/0.71 | 0.63/0.74 | 0.60/0.72 | 0.62/0.74 | 341.63 |
| Qwen2.5-32B-Instruct | 0.61/0.72 | 0.49/0.58 | 0.60/0.71 | 0.55/0.67 | _0.65_/0.75 | 331.34 |
| Phi-4 | 0.56/0.66 | 0.55/0.64 | _0.57_/0.67 | 0.56/0.66 | _0.57_/0.67 | 294.33 |
| Meta-Llama-3.1-8B-Instruct | _0.48_/0.60 | 0.41/0.54 | 0.44/0.56 | 0.40/0.53 | 0.47/0.59 | 338.38 |
| Mistral-Nemo-Instruct-2407 | _0.46_/0.55 | 0.32/0.42 | 0.45/0.56 | 0.44/0.55 | 0.44/0.54 | 231.52 |
| Avg Tokens by Prompt | 283.04 | 151.97 | 437.38 | 334.71 | 317.57 | 304.93 |
## Citation
```bash
@inproceedings{
title = "SECQUE: A Benchmark for Evaluating Real-World Financial Analysis Capabilitiese",
author = "Ben Yoash, Noga and
Brief, Meni and
Ovadia, Oded and
Shenderovitz, Gil and
Mishaeli, Moshik and
Lemberg, Rachel and
Sheetrit, Eitam",
month = apr,
year = "2025",
url = "https://arxiv.org/pdf/2504.04596",
}
```
## Evaluation Benchmark notice
This benchmark is indented solely for evaluation, and must not be used for training in any way. |