Developing an ESG-Oriented Large Language Model through ESG Practices

Abstract

Environmental, Social, and Governance (ESG) considerations play a central role in contemporary financial decision-making. In parallel, Large Language Model (LLM) applications in this domain have primarily emphasized well-defined discriminative tasks, such as classification or scoring, which have proven effective for structured analysis and benchmarking. However, this prevailing focus offers limited support for more interactive and generative ESG scenarios, where embedded domain knowledge and contextual understanding are essential. In this work, we propose an ESG-oriented adaptation pipeline for LLMs that integrates ESG principles not only as a target domain, but also as guiding constraints throughout training and evaluation. Building on the Qwen-3-4B architecture, we explore parameter-efficient adaptation strategies using Low-Rank Adaptation (LoRA) and the Instruction-Residual Method (IRM) to produce three ESG-specialized models. We evaluate the proposed models on ESG question answering under both zero-shot and knowledge-augmented settings, using a diverse set of generative, semantic, readability, and environmental impact metrics. Our results show that the ESG-adapted models consistently outperform their original counterparts and competitive baselines such as Llama-3 and Gemma-3. Although limitations remain in tool-based knowledge integration, this work establishes a foundation for ESG-oriented language generation and highlights the importance of responsible, domain-aware LLM adaptation.