# CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools

arXiv: https://arxiv.org/abs/2307.15770 · HTML: https://mlsanity.com/paper/2307.15770/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 83 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 28 of 28)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [Large Language Models Are State-of-the-Art Evaluators of Translation Quality](https://mlsanity.com/paper/2302.14520/) (2023)
- [Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback](https://mlsanity.com/paper/2302.12813/) (2023)
- [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://mlsanity.com/paper/2012.00614/) (2020)
- [LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization](https://mlsanity.com/paper/2301.13298/) (2023)
- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [Automatic Evaluation of Attribution by Large Language Models](https://mlsanity.com/paper/2305.06311/) (2023)
- [Detecting Stance in Media on Global Warming](https://mlsanity.com/paper/2010.15149/) (2020)
- [Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback](https://mlsanity.com/paper/2005.02539/) (2020)
- [Active Learning for Graph Neural Networks via Node Feature Propagation](https://mlsanity.com/paper/1910.07567/) (2019)
- [Learning to Repair: Repairing model output errors after deployment using a dynamic memory of feedback](https://mlsanity.com/paper/2112.09737/) (2021)
- [chatClimate: Grounding Conversational AI in Climate Science](https://mlsanity.com/paper/2304.05510/) (2023)
- [Analyzing Sustainability Reports Using Natural Language Processing](https://mlsanity.com/paper/2011.08073/) (2020)
- [Environmental Claim Detection](https://mlsanity.com/paper/2209.00507/) (2022)
- [Interscript: A dataset for interactive learning of scripts through error feedback](https://mlsanity.com/paper/2112.07867/) (2021)

## Built on by (most cited first, 29 of 29)

- [Large Language Models Hallucination: A Comprehensive Survey](https://mlsanity.com/paper/2510.06265/) (2025)
- [AI Hallucinations: A Misnomer Worth Clarifying](https://mlsanity.com/paper/2401.06796/) (2024)
- [MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents](https://mlsanity.com/paper/2502.00415/) (2025)
- [ESGReveal: An LLM-based approach for extracting structured data from ESG reports](https://mlsanity.com/paper/2312.17264/) (2023)
- [ClimRetrieve: A Benchmarking Dataset for Information Retrieval from Corporate Climate Disclosures](https://mlsanity.com/paper/2406.09818/) (2024)
- [Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering](https://mlsanity.com/paper/2402.08277/) (2024)
- [Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysis](https://mlsanity.com/paper/2406.10273/) (2024)
- [Explainable Natural Language Processing for Corporate Sustainability Analysis](https://mlsanity.com/paper/2407.17487/) (2024)
- [SPHERE: An Evaluation Card for Human-AI Systems](https://mlsanity.com/paper/2504.07971/) (2025)
- [ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge](https://mlsanity.com/paper/2506.01646/) (2025)
- [SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation](https://mlsanity.com/paper/2412.10906/) (2024)
- [Towards Robust ESG Analysis Against Greenwashing Risks: Aspect-Action Analysis with Cross-Category Generalization](https://mlsanity.com/paper/2502.15821/) (2025)
- [A Scoping Review of ChatGPT Research in Accounting and Finance](https://mlsanity.com/paper/2412.05731/) (2024)
- [SustainableQA: A Comprehensive Question Answering Dataset for Corporate Sustainability and EU Taxonomy Reporting](https://mlsanity.com/paper/2508.03000/) (2025)
- [DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation](https://mlsanity.com/paper/2406.14162/) (2024)
- [On the Reliability of Large Language Models to Misinformed and Demographically-Informed Prompts](https://mlsanity.com/paper/2410.10850/) (2024)
- [Enhancing Retrieval for ESGLLM via ESG-CID -- A Disclosure Content Index Finetuning Dataset for Mapping GRI and ESRS](https://mlsanity.com/paper/2503.10674/) (2025)
- [pdfQA: Diverse, Challenging, and Realistic Question Answering over PDFs](https://mlsanity.com/paper/2601.02285/) (2026)
- [ESGLens: An LLM-Based RAG Framework for Interactive ESG Report Analysis and Score Prediction](https://mlsanity.com/paper/2604.19779/) (2026)
- [Exploring Nature: Datasets and Models for Analyzing Nature-Related Disclosures](https://mlsanity.com/paper/2312.17337/) (2023)
- [Think-on-Process: Dynamic Process Generation for Collaborative Development of Multi-Agent System](https://mlsanity.com/paper/2409.06568/) (2024)
- [AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statements](https://mlsanity.com/paper/2502.07022/) (2025)
- [Judging It, Washing It: Scoring and Greenwashing Corporate Climate Disclosures using Large Language Models](https://mlsanity.com/paper/2502.15094/) (2025)
- [UsefulBench: Towards Decision-Useful Information as a Target for Information Retrieval](https://mlsanity.com/paper/2604.15827/) (2026)
- [Assisting humans in complex comparisons: automated information comparison at scale](https://mlsanity.com/paper/2404.04351/) (2024)
- [Dhoroni: Exploring Bengali Climate Change and Environmental Views with a Multi-Perspective News Dataset and Natural Language Processing](https://mlsanity.com/paper/2410.17225/) (2024)
- [Climate AI for Corporate Decarbonization Metrics Extraction](https://mlsanity.com/paper/2411.03402/) (2024)
- [EmeraldMind: A Knowledge Graph-Augmented Framework for Greenwashing Detection](https://mlsanity.com/paper/2512.11506/) (2025)
- [Evidence-Grounded Auditing of Identification Assumptions in Climate-Policy Causal Evaluations](https://mlsanity.com/paper/2609.30867/) (2026)

## Abstract (arXiv)

In the face of climate change, are companies really taking substantial steps toward more sustainable operations? A comprehensive answer lies in the dense, information-rich landscape of corporate sustainability reports. However, the sheer volume and complexity of these reports make human analysis very costly. Therefore, only a few entities worldwide have the resources to analyze these reports at scale, which leads to a lack of transparency in sustainability reporting. Empowering stakeholders with LLM-based automatic analysis tools can be a promising way to democratize sustainability report analysis. However, developing such tools is challenging due to (1) the hallucination of LLMs and (2) the inefficiency of bringing domain experts into the AI development loop. In this paper, we ChatReport, a novel LLM-based system to automate the analysis of corporate sustainability reports, addressing existing challenges by (1) making the answers traceable to reduce the harm of hallucination and (2) actively involving domain experts in the development loop. We make our methodology, annotated datasets, and generated analyses of 1015 reports publicly available.