# Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization

arXiv: https://arxiv.org/abs/2502.15821 · HTML: https://mlsanity.com/paper/2502.15821/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 59 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 19 of 19)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [On Feature Learning in the Presence of Spurious Correlations](https://mlsanity.com/paper/2210.11369/) (2022)
- [Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era](https://mlsanity.com/paper/2404.11457/) (2024)
- [Towards Mitigating Hallucination in Large Language Models via Self-Reflection](https://mlsanity.com/paper/2310.06271/) (2023)
- [Discriminative Adversarial Domain Adaptation](https://mlsanity.com/paper/1911.12036/) (2019)
- [The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs](https://mlsanity.com/paper/2210.14986/) (2022)
- [Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2111.02194/) (2021)
- [CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools](https://mlsanity.com/paper/2307.15770/) (2023)
- [Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination](https://mlsanity.com/paper/2311.09627/) (2023)
- [GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2009.10557/) (2020)
- [Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies](https://mlsanity.com/paper/2501.17030/) (2025)
- [Explainable Natural Language Processing for Corporate Sustainability Analysis](https://mlsanity.com/paper/2407.17487/) (2024)
- [BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2406.07365/) (2024)
- [ESGSenticNet: A Neurosymbolic Knowledge Base for Corporate Sustainability Analysis](https://mlsanity.com/paper/2501.15720/) (2025)

## Built on by (most cited first, 7 of 7)

- [ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge](https://mlsanity.com/paper/2506.01646/) (2025)
- [Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation](https://mlsanity.com/paper/2505.19430/) (2025)
- [Advancing ESG Intelligence: An Expert-level Agent and Comprehensive Benchmark for Sustainable Finance](https://mlsanity.com/paper/2601.08676/) (2026)
- [DeepGreen: Effective LLM-Driven Greenwashing Monitoring System Designed for Empirical Testing -- Evidence from China](https://mlsanity.com/paper/2504.07733/) (2025)
- [Pharos-ESG: A Framework for Multimodal Parsing, Contextual Narration, and Hierarchical Labeling of ESG Report](https://mlsanity.com/paper/2511.16417/) (2025)
- [Enhancing Language Models for Robust Greenwashing Detection](https://mlsanity.com/paper/2601.21722/) (2026)
- [What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification](https://mlsanity.com/paper/2607.17952/) (2026)

## Abstract (arXiv)

Sustainability reports are key for evaluating companies' environmental, social and governance, ESG performance, but their content is increasingly obscured by greenwashing - sustainability claims that are misleading, exaggerated, and fabricated. Yet, existing NLP approaches for ESG analysis lack robustness against greenwashing risks, often extracting insights that reflect misleading or exaggerated sustainability claims rather than objective ESG performance. To bridge this gap, we introduce A3CG - Aspect-Action Analysis with Cross-Category Generalization, as a novel dataset to improve the robustness of ESG analysis amid the prevalence of greenwashing. By explicitly linking sustainability aspects with their associated actions, A3CG facilitates a more fine-grained and transparent evaluation of sustainability claims, ensuring that insights are grounded in verifiable actions rather than vague or misleading rhetoric. Additionally, A3CG emphasizes cross-category generalization. This ensures robust model performance in aspect-action analysis even when companies change their reports to selectively favor certain sustainability areas. Through experiments on A3CG, we analyze state-of-the-art supervised models and LLMs, uncovering their limitations and outlining key directions for future research.