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arXiv · 2504.16188

FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking

Jabez Magomere·Elena Kochkina·Samuel Mensah·Simerjot Kaur·Charese H. Smiley
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Abstract

We introduce FinNLI, a benchmark dataset for Financial Natural Language Inference (FinNLI) across diverse financial texts like SEC Filings, Annual Reports, and Earnings Call transcripts. Our dataset framework ensures diverse premise-hypothesis pairs while minimizing spurious correlations. FinNLI comprises 21,304 pairs, including a high-quality test set of 3,304 instances annotated by finance experts. Evaluations show that domain shift significantly degrades general-domain NLI performance. The highest Macro F1 scores for pre-trained (PLMs) and large language models (LLMs) baselines are 74.57% and 78.62%, respectively, highlighting the dataset's difficulty. Surprisingly, instruction-tuned financial LLMs perform poorly, suggesting limited generalizability. FinNLI exposes weaknesses in current LLMs for financial reasoning, indicating room for improvement.

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Jabez Magomere, Elena Kochkina, Samuel Mensah, Simerjot Kaur, Charese H. Smiley. 2025-04-22. FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking. https://arxiv.org/abs/2504.16188

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