FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909588833435648 |
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| author | Magomere, Jabez Kochkina, Elena Mensah, Samuel Kaur, Simerjot Smiley, Charese H. |
| author_facet | Magomere, Jabez Kochkina, Elena Mensah, Samuel Kaur, Simerjot Smiley, Charese H. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16188 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking Magomere, Jabez Kochkina, Elena Mensah, Samuel Kaur, Simerjot Smiley, Charese H. Computation and Language Artificial Intelligence Machine Learning 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. |
| title | FinNLI: Novel Dataset for Multi-Genre Financial Natural Language Inference Benchmarking |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.16188 |