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Open-source LLMs evaluated for ESG reporting tasks · 1 source tracked

By PulseAugur Editorial · [1 sources] ·

A new paper evaluates the performance of seven open-source large language models (LLMs) for retrieval-augmented generation (RAG) tasks specifically within the environmental, social, and governance (ESG) domain. The study utilized 498 real-world ESG reports from EU-listed companies and a set of 100 synthetic QA pairs to assess models like GLM 4.7 Flash, Nemotron-3-nano:4b, and Qwen3:4b-instruct. While retrieval performance was generally strong across models, generation metrics like faithfulness and factual correctness showed significant variation, indicating a need for domain-specific fine-tuning. AI

IMPACT Provides data-driven guidance for selecting and fine-tuning open-source LLMs for specialized ESG reporting tasks.

RANK_REASON The cluster contains an academic paper evaluating open-source LLMs on a specific domain task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Open-source LLMs evaluated for ESG reporting tasks · 1 source tracked

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The cluster contains an academic paper evaluating open-source LLMs on a specific domain task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Motaz Saad, Anna Borrelli, Ivan Gentile, Kianna Kazemi, Francesco Piccialli, Antonella Longo ·

    Empirical Evaluation of Open-Source Large Language Models for Retrieval-Augmented Generation in ESG Domain

    arXiv:2609.15242v1 Announce Type: new Abstract: Environmental, Social, and Governance (ESG) reporting is critical for corporate accountability, with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) offering strong potential to automate KPI extraction. However…

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