ESGBench: A Benchmark for Explainable ESG Question Answering in Corporate Sustainability Reports
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914165648523264 |
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| author | George, Sherine Saji, Nithish |
| author_facet | George, Sherine Saji, Nithish |
| contents | We present ESGBench, a benchmark dataset and evaluation framework designed to assess explainable ESG question answering systems using corporate sustainability reports. The benchmark consists of domain-grounded questions across multiple ESG themes, paired with human-curated answers and supporting evidence to enable fine-grained evaluation of model reasoning. We analyze the performance of state-of-the-art LLMs on ESGBench, highlighting key challenges in factual consistency, traceability, and domain alignment. ESGBench aims to accelerate research in transparent and accountable ESG-focused AI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16438 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ESGBench: A Benchmark for Explainable ESG Question Answering in Corporate Sustainability Reports George, Sherine Saji, Nithish Computation and Language Information Retrieval I.2.7; H.3.3 We present ESGBench, a benchmark dataset and evaluation framework designed to assess explainable ESG question answering systems using corporate sustainability reports. The benchmark consists of domain-grounded questions across multiple ESG themes, paired with human-curated answers and supporting evidence to enable fine-grained evaluation of model reasoning. We analyze the performance of state-of-the-art LLMs on ESGBench, highlighting key challenges in factual consistency, traceability, and domain alignment. ESGBench aims to accelerate research in transparent and accountable ESG-focused AI systems. |
| title | ESGBench: A Benchmark for Explainable ESG Question Answering in Corporate Sustainability Reports |
| topic | Computation and Language Information Retrieval I.2.7; H.3.3 |
| url | https://arxiv.org/abs/2511.16438 |