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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.15985 |
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| _version_ | 1866913747674595328 |
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| author | Yuan, Han Zhang, Li Ma, Zheng |
| author_facet | Yuan, Han Zhang, Li Ma, Zheng |
| contents | Language models (LMs) have exhibited exceptional versatility in reasoning and in-depth financial analysis through their proprietary information processing capabilities. Previous research focused on evaluating classification performance while often overlooking explainability or pre-conceived that refined explanation corresponds to higher classification accuracy. Using a public dataset in finance domain, we quantitatively evaluated self-explanations by LMs, focusing on their factuality and causality. We identified the statistically significant relationship between the accuracy of classifications and the factuality or causality of self-explanations. Our study built an empirical foundation for approximating classification confidence through self-explanations and for optimizing classification via proprietary reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15985 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring the Reliability of Self-explanation and its Relationship with Classification in Language Model-driven Financial Analysis Yuan, Han Zhang, Li Ma, Zheng Artificial Intelligence Language models (LMs) have exhibited exceptional versatility in reasoning and in-depth financial analysis through their proprietary information processing capabilities. Previous research focused on evaluating classification performance while often overlooking explainability or pre-conceived that refined explanation corresponds to higher classification accuracy. Using a public dataset in finance domain, we quantitatively evaluated self-explanations by LMs, focusing on their factuality and causality. We identified the statistically significant relationship between the accuracy of classifications and the factuality or causality of self-explanations. Our study built an empirical foundation for approximating classification confidence through self-explanations and for optimizing classification via proprietary reasoning. |
| title | Exploring the Reliability of Self-explanation and its Relationship with Classification in Language Model-driven Financial Analysis |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2503.15985 |