Pith. sign in

REVIEW 3 cited by

CHATREPORT: Democratizing Sustainability Disclosure Analysis through LLM-based Tools

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2307.15770 v2 pith:HQWDL7AC submitted 2023-07-28 cs.CL cs.AI

classification cs.CLcs.AI
keywords analysisreportssustainabilityllm-basedtoolschatreportcorporatedevelopment
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 6 citations worldwide. Full citation record

  1. AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statements

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Introduces AIMS.au, a 5,731-statement, sentence-level annotated dataset for detecting disclosures mandated by Australia's Modern Slavery Act, with benchmarks showing fine-tuned models outperform zero-shot LLMs.

  2. From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents

    cs.AI 2026-03 unverdicted novelty 5.0 of 10

    Step-wise conformal labels plus linear probes recover linearly separable success/failure directions in LLM agents on ScienceWorld and AlfWorld, with preliminary steering gains.

  3. The Accuracy, Robustness, and Readability of LLM-Generated Sustainability-Related Word Definitions

    cs.CL 2025-02 conditional novelty 5.0 of 10

    LLM-generated definitions of IPCC climate terms achieve moderate semantic similarity (0.57 to 0.59) to official definitions and lower readability than the originals.

Pith tools