Greench-v1: distilling SLMs on Greenwashing Detection

Abstract

Validating greenwashing claims in environmental, social, and governance (ESG) reports relies heavily on costly and inconsistent manual review. To address this, this paper introduces Greench-v1, a low-latency small language model (based on Qwen3-4B) that screens ESG text at the paragraph level. The model outputs a three-way classification (Greenwashing Alert, No Greenwashing, Not Relevant) paired with a concise, paragraph-grounded rationale to assist human auditors in triage and validation. The system was trained on a custom dataset of roughly 2,000 paragraphs, adapted from the ClimateBERT corpus. This dataset mitigates class imbalance through controlled paraphrasing of rare positive instances and uses GPT-4o to generate evidence-based justifications. Four training regimes were evaluated: (i) Hard distillation: Supervised fine-tuning on teacher-generated outputs. (ii) Soft distillation: Training the student to match the temperature-scaled logits of a domain-specialized Qwen3-14B teacher. (iii) Group Relative Policy Optimization (GRPO): Reward-based updates driven by exact-match alert generation. (iv) Hybrid GRPO: GRPO initialized from the hard-distilled checkpoint. Distillation and efficient policy optimization significantly improved performance over untuned baselines. Soft distillation and GRPO achieved the strongest results, increasing the "Greenwashing Alert" weighted F1-score by 36.7% and 49.0%, respectively, resulting in a deployable tool for screening large volumes of ESG narratives.

Resources

Details