@inproceedings{wrzalik-etal-2025-integrating,
title = "Integrating Expert Labels into {LLM}-based Emission Goal Detection: Example Selection vs Automatic Prompt Design",
author = "Wrzalik, Marco and
Ulges, Adrian and
Uersfeld, Anne and
Faust, Florian and
Campos, Viola",
editor = "Dutia, Kalyan and
Henderson, Peter and
Leippold, Markus and
Manning, Christoper and
Morio, Gaku and
Muccione, Veruska and
Ni, Jingwei and
Schimanski, Tobias and
Stammbach, Dominik and
Singh, Alok and
Su, Alba (Ruiran) and
A. Vaghefi, Saeid",
booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.climatenlp-1.5/",
doi = "10.18653/v1/2025.climatenlp-1.5",
pages = "68--75",
ISBN = "979-8-89176-259-6",
abstract = "We address the detection of emission reduction goals in corporate reports, an important task for monitoring companies' progress in addressing climate change. Specifically, we focus on the issue of integrating expert feedback in the form of labeled example passages into LLM-based pipelines, and compare the two strategies of (1) a dynamic selection of few-shot examples and (2) the automatic optimization of the prompt by the LLM itself. Our findings on a public dataset of 769 climate-related passages from real-world business reports indicate that automatic prompt optimization is the superior approach, while combining both methods provides only limited benefit. Qualitative results indicate that optimized prompts do indeed capture many intricacies of the targeted emission goal extraction task."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wrzalik-etal-2025-integrating">
<titleInfo>
<title>Integrating Expert Labels into LLM-based Emission Goal Detection: Example Selection vs Automatic Prompt Design</title>
</titleInfo>
<name type="personal">
<namePart type="given">Marco</namePart>
<namePart type="family">Wrzalik</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Adrian</namePart>
<namePart type="family">Ulges</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anne</namePart>
<namePart type="family">Uersfeld</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Florian</namePart>
<namePart type="family">Faust</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viola</namePart>
<namePart type="family">Campos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kalyan</namePart>
<namePart type="family">Dutia</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Peter</namePart>
<namePart type="family">Henderson</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Markus</namePart>
<namePart type="family">Leippold</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Christoper</namePart>
<namePart type="family">Manning</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gaku</namePart>
<namePart type="family">Morio</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veruska</namePart>
<namePart type="family">Muccione</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jingwei</namePart>
<namePart type="family">Ni</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tobias</namePart>
<namePart type="family">Schimanski</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dominik</namePart>
<namePart type="family">Stammbach</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alok</namePart>
<namePart type="family">Singh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alba</namePart>
<namePart type="given">(Ruiran)</namePart>
<namePart type="family">Su</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saeid</namePart>
<namePart type="family">A. Vaghefi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-259-6</identifier>
</relatedItem>
<abstract>We address the detection of emission reduction goals in corporate reports, an important task for monitoring companies’ progress in addressing climate change. Specifically, we focus on the issue of integrating expert feedback in the form of labeled example passages into LLM-based pipelines, and compare the two strategies of (1) a dynamic selection of few-shot examples and (2) the automatic optimization of the prompt by the LLM itself. Our findings on a public dataset of 769 climate-related passages from real-world business reports indicate that automatic prompt optimization is the superior approach, while combining both methods provides only limited benefit. Qualitative results indicate that optimized prompts do indeed capture many intricacies of the targeted emission goal extraction task.</abstract>
<identifier type="citekey">wrzalik-etal-2025-integrating</identifier>
<identifier type="doi">10.18653/v1/2025.climatenlp-1.5</identifier>
<location>
<url>https://aclanthology.org/2025.climatenlp-1.5/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>68</start>
<end>75</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Integrating Expert Labels into LLM-based Emission Goal Detection: Example Selection vs Automatic Prompt Design
%A Wrzalik, Marco
%A Ulges, Adrian
%A Uersfeld, Anne
%A Faust, Florian
%A Campos, Viola
%Y Dutia, Kalyan
%Y Henderson, Peter
%Y Leippold, Markus
%Y Manning, Christoper
%Y Morio, Gaku
%Y Muccione, Veruska
%Y Ni, Jingwei
%Y Schimanski, Tobias
%Y Stammbach, Dominik
%Y Singh, Alok
%Y Su, Alba (Ruiran)
%Y A. Vaghefi, Saeid
%S Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-259-6
%F wrzalik-etal-2025-integrating
%X We address the detection of emission reduction goals in corporate reports, an important task for monitoring companies’ progress in addressing climate change. Specifically, we focus on the issue of integrating expert feedback in the form of labeled example passages into LLM-based pipelines, and compare the two strategies of (1) a dynamic selection of few-shot examples and (2) the automatic optimization of the prompt by the LLM itself. Our findings on a public dataset of 769 climate-related passages from real-world business reports indicate that automatic prompt optimization is the superior approach, while combining both methods provides only limited benefit. Qualitative results indicate that optimized prompts do indeed capture many intricacies of the targeted emission goal extraction task.
%R 10.18653/v1/2025.climatenlp-1.5
%U https://aclanthology.org/2025.climatenlp-1.5/
%U https://doi.org/10.18653/v1/2025.climatenlp-1.5
%P 68-75
Markdown (Informal)
[Integrating Expert Labels into LLM-based Emission Goal Detection: Example Selection vs Automatic Prompt Design](https://aclanthology.org/2025.climatenlp-1.5/) (Wrzalik et al., ClimateNLP 2025)
ACL