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.

翻译：环境、社会与治理（ESG）考量在当代金融决策中占据核心地位。与此同时，大语言模型在该领域的应用主要侧重于分类或评分等明确定义的判别性任务，这些任务已被证明适用于结构化分析与基准测试。然而，这种主流关注对更具交互性和生成性的ESG场景支持有限，而这类场景需要嵌入的领域知识与情境理解能力。本研究提出一套面向ESG的大语言模型适配流程，将ESG原则不仅作为目标领域，更作为贯穿训练与评估过程的指导性约束。基于Qwen-3-4B架构，我们探索采用低秩适配与指令残差方法的参数高效适配策略，构建三个ESG专用模型。在零样本与知识增强两种设置下，我们通过包含生成质量、语义相关性、可读性及环境影响等多样化指标对提出模型进行ESG问答评估。结果表明，ESG适配模型在各项指标上持续优于原始模型及Llama-3、Gemma-3等竞争性基线。尽管在基于工具的知识整合方面仍存在局限性，本研究为面向ESG的语言生成奠定了方法论基础，凸显了负责任、领域感知式大语言模型适配的重要性。