Climate Finance Bench
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866916765360979968 |
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| author | Mankour, Rafik Chafai, Yassine Saleh, Hamada Hassine, Ghassen Ben Barreau, Thibaud Tankov, Peter |
| author_facet | Mankour, Rafik Chafai, Yassine Saleh, Hamada Hassine, Ghassen Ben Barreau, Thibaud Tankov, Peter |
| contents | Climate Finance Bench introduces an open benchmark that targets question-answering over corporate climate disclosures using Large Language Models. We curate 33 recent sustainability reports in English drawn from companies across all 11 GICS sectors and annotate 330 expert-validated question-answer pairs that span pure extraction, numerical reasoning, and logical reasoning. Building on this dataset, we propose a comparison of RAG (retrieval-augmented generation) approaches. We show that the retriever's ability to locate passages that actually contain the answer is the chief performance bottleneck. We further argue for transparent carbon reporting in AI-for-climate applications, highlighting advantages of techniques such as Weight Quantization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22752 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Climate Finance Bench Mankour, Rafik Chafai, Yassine Saleh, Hamada Hassine, Ghassen Ben Barreau, Thibaud Tankov, Peter Computation and Language Climate Finance Bench introduces an open benchmark that targets question-answering over corporate climate disclosures using Large Language Models. We curate 33 recent sustainability reports in English drawn from companies across all 11 GICS sectors and annotate 330 expert-validated question-answer pairs that span pure extraction, numerical reasoning, and logical reasoning. Building on this dataset, we propose a comparison of RAG (retrieval-augmented generation) approaches. We show that the retriever's ability to locate passages that actually contain the answer is the chief performance bottleneck. We further argue for transparent carbon reporting in AI-for-climate applications, highlighting advantages of techniques such as Weight Quantization. |
| title | Climate Finance Bench |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.22752 |