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.
@article{arxiv.2511.16438,
title = {ESGBench: A Benchmark for Explainable ESG Question Answering in Corporate Sustainability Reports},
author = {Sherine George and Nithish Saji},
journal= {arXiv preprint arXiv:2511.16438},
year = {2025}
}
Comments
Workshop paper accepted at AI4DF 2025 (part of ACM ICAIF 2025). 3 pages including tables and figures