@inproceedings{fedorova-etal-2026-preference,
title = "Preference Optimization Improves Definition Generation: Semantic Change in {EU} Institutional Sustainable Finance Disclosures",
author = "Fedorova, Mariia and
Kutuzov, Andrey and
Delascio, Gustavo Cadenas",
editor = "Atwell, Katherine and
Borah, Angana and
Dementieva, Daryna and
Kreiss, Elisa and
Kotonya, Neema and
Liu, Jiarui and
Olson, Liz and
Wan, Ruyuan and
Zhao, Jieyu",
booktitle = "Proceedings of the Fifth Workshop on {NLP} for Positive Impact ({NLP}4{PI})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlp4pi-1.30/",
pages = "349--359",
ISBN = "979-8-89176-495-8",
abstract = "Dictionary-like definition generators are known to be useful for lexical semantic change detection (LSCD). However, they suffer from overly granular sense distribution, producing excessive number of definitions for one and the same lexical sense. In this work, we show that fine-tuning with Direct Preference Optimization (DPO) helps to overcome this problem. We test our approach both quantitatively on a curated academic LSCD benchmark and qualitatively on real-world data for a practical task of auditing diachronic changes in sustainability terminology within project documentation of EU development finance.All models, code and predictions produced in this study are publicly available at https://github.com/ltgoslo/dpo-for-definition-generation."
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<abstract>Dictionary-like definition generators are known to be useful for lexical semantic change detection (LSCD). However, they suffer from overly granular sense distribution, producing excessive number of definitions for one and the same lexical sense. In this work, we show that fine-tuning with Direct Preference Optimization (DPO) helps to overcome this problem. We test our approach both quantitatively on a curated academic LSCD benchmark and qualitatively on real-world data for a practical task of auditing diachronic changes in sustainability terminology within project documentation of EU development finance.All models, code and predictions produced in this study are publicly available at https://github.com/ltgoslo/dpo-for-definition-generation.</abstract>
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%0 Conference Proceedings
%T Preference Optimization Improves Definition Generation: Semantic Change in EU Institutional Sustainable Finance Disclosures
%A Fedorova, Mariia
%A Kutuzov, Andrey
%A Delascio, Gustavo Cadenas
%Y Atwell, Katherine
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Kreiss, Elisa
%Y Kotonya, Neema
%Y Liu, Jiarui
%Y Olson, Liz
%Y Wan, Ruyuan
%Y Zhao, Jieyu
%S Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-495-8
%F fedorova-etal-2026-preference
%X Dictionary-like definition generators are known to be useful for lexical semantic change detection (LSCD). However, they suffer from overly granular sense distribution, producing excessive number of definitions for one and the same lexical sense. In this work, we show that fine-tuning with Direct Preference Optimization (DPO) helps to overcome this problem. We test our approach both quantitatively on a curated academic LSCD benchmark and qualitatively on real-world data for a practical task of auditing diachronic changes in sustainability terminology within project documentation of EU development finance.All models, code and predictions produced in this study are publicly available at https://github.com/ltgoslo/dpo-for-definition-generation.
%U https://aclanthology.org/2026.nlp4pi-1.30/
%P 349-359
Markdown (Informal)
[Preference Optimization Improves Definition Generation: Semantic Change in EU Institutional Sustainable Finance Disclosures](https://aclanthology.org/2026.nlp4pi-1.30/) (Fedorova et al., NLP4PI 2026)
ACL