ESGBench: A Benchmark for Explainable ESG Question Answering in Corporate Sustainability Reports
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Abstract (arXiv)
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.
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References · 3
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2020FinBERT: A Pretrained Language Model for Financial Communicationscited by 161
- 2021ClimateBert: A Pretrained Language Model for Climate-Related Textcited by 90
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