@inproceedings{kolli-etal-2025-automated,
title = "Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual {RAG} Approach",
author = "Kolli, Imene and
Vaghefi, Saeid and
Senni, Chiara Colesanti and
Raj, Shantam and
Leippold, Markus",
editor = {Habernal, Ivan and
Schulam, Peter and
Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-demos.9/",
pages = "111--129",
ISBN = "979-8-89176-334-0",
abstract = "InfluenceMap{'}s LobbyMap Platform monitors the climate policy engagement of over 500 companies and 250 industry associations, assessing each entity{'}s support or opposition to science-based policy pathways for achieving the Paris Agreement{'}s goal of limiting global warming to 1.5{\textdegree}C. Although InfluenceMap has made progress with automating key elements of the analytical workflow, a significant portion of the assessment remains manual, making it time- and labor-intensive and susceptible to human error. We propose an AI-assisted framework to accelerate the monitoring of corporate climate policy engagement by leveraging Retrieval-Augmented Generation to automate the most time-intensive extraction of relevant evidence from large-scale textual data. Our evaluation shows that a combination of layout-aware parsing, the Nomic embedding model, and few-shot prompting strategies yields the best performance in extracting and classifying evidence from multilingual corporate documents. We conclude that while the automated RAG system effectively accelerates evidence extraction, the nuanced nature of the analysis necessitates a human-in-the-loop approach where the technology augments, rather than replaces, expert judgment to ensure accuracy."
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<abstract>InfluenceMap’s LobbyMap Platform monitors the climate policy engagement of over 500 companies and 250 industry associations, assessing each entity’s support or opposition to science-based policy pathways for achieving the Paris Agreement’s goal of limiting global warming to 1.5°C. Although InfluenceMap has made progress with automating key elements of the analytical workflow, a significant portion of the assessment remains manual, making it time- and labor-intensive and susceptible to human error. We propose an AI-assisted framework to accelerate the monitoring of corporate climate policy engagement by leveraging Retrieval-Augmented Generation to automate the most time-intensive extraction of relevant evidence from large-scale textual data. Our evaluation shows that a combination of layout-aware parsing, the Nomic embedding model, and few-shot prompting strategies yields the best performance in extracting and classifying evidence from multilingual corporate documents. We conclude that while the automated RAG system effectively accelerates evidence extraction, the nuanced nature of the analysis necessitates a human-in-the-loop approach where the technology augments, rather than replaces, expert judgment to ensure accuracy.</abstract>
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%0 Conference Proceedings
%T Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual RAG Approach
%A Kolli, Imene
%A Vaghefi, Saeid
%A Senni, Chiara Colesanti
%A Raj, Shantam
%A Leippold, Markus
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F kolli-etal-2025-automated
%X InfluenceMap’s LobbyMap Platform monitors the climate policy engagement of over 500 companies and 250 industry associations, assessing each entity’s support or opposition to science-based policy pathways for achieving the Paris Agreement’s goal of limiting global warming to 1.5°C. Although InfluenceMap has made progress with automating key elements of the analytical workflow, a significant portion of the assessment remains manual, making it time- and labor-intensive and susceptible to human error. We propose an AI-assisted framework to accelerate the monitoring of corporate climate policy engagement by leveraging Retrieval-Augmented Generation to automate the most time-intensive extraction of relevant evidence from large-scale textual data. Our evaluation shows that a combination of layout-aware parsing, the Nomic embedding model, and few-shot prompting strategies yields the best performance in extracting and classifying evidence from multilingual corporate documents. We conclude that while the automated RAG system effectively accelerates evidence extraction, the nuanced nature of the analysis necessitates a human-in-the-loop approach where the technology augments, rather than replaces, expert judgment to ensure accuracy.
%U https://aclanthology.org/2025.emnlp-demos.9/
%P 111-129
Markdown (Informal)
[Automated Evidence Extraction and Scoring for Corporate Climate Policy Engagement: A Multilingual RAG Approach](https://aclanthology.org/2025.emnlp-demos.9/) (Kolli et al., EMNLP 2025)
ACL