ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge
Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Xiaoqiao Wang, Wei Liu, Chunyan Miao

TL;DR
ESGenius is a new benchmark for evaluating LLMs on ESG and sustainability questions, revealing current models' limitations and the benefits of retrieval-augmented methods for improved performance.
Contribution
This paper introduces ESGenius, the first comprehensive benchmark for assessing LLMs' ESG knowledge, including a validated question set and a source corpus, with a two-stage evaluation protocol.
Findings
State-of-the-art LLMs achieve 55-70% accuracy in zero-shot settings.
Retrieval-Augmented Generation significantly improves model performance.
Grounding responses in authoritative sources enhances ESG understanding.
Abstract
We introduce ESGenius, a comprehensive benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in Environmental, Social, and Governance (ESG) and sustainability-focused question answering. ESGenius comprises two key components: (i) ESGenius-QA, a collection of 1,136 Multiple-Choice Questions (MCQs) generated by LLMs and rigorously validated by domain experts, covering a broad range of ESG pillars and sustainability topics. Each question is systematically linked to its corresponding source text, enabling transparent evaluation and supporting Retrieval-Augmented Generation (RAG) methods; and (ii) ESGenius-Corpus, a meticulously curated repository of 231 foundational frameworks, standards, reports, and recommendation documents from 7 authoritative sources. Moreover, to fully assess the capabilities and adaptation potential of LLMs, we implement a rigorous…
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Taxonomy
TopicsLaw, AI, and Intellectual Property · Regulation and Compliance Studies · Environmental law and policy
