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ESGBench: A Benchmark for Explainable ESG Question Answering in Corporate Sustainability Reports

arXiv 2511.16438 ↗First posted 20 Nov 2025cs.CL cs.IRcited by 0

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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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 8 references that are in the map. First posted on arXiv 20 Nov 2025.

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References · 3

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020FinBERT: A Pretrained Language Model for Financial Communicationscited by 161
  3. 2021ClimateBert: A Pretrained Language Model for Climate-Related Textcited by 90

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