@inproceedings{li-etal-2026-transfers,
title = "What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification",
author = "Li, Guosheng and
Ren, Fenghui and
Liu, Bin and
Yu, Chuan and
Ji, Kaiying and
Yue, Lin and
Shen, Jun and
Qian, Sasa",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1825/",
pages = "38975--38989",
ISBN = "979-8-89176-462-0",
abstract = "Climate disclosure classification is a fundamental task for analysing corporate climate disclosures, yet such disclosures appear in many different sources{---}annual reports, press releases, and earnings calls{---}that differ in length, purpose, and writing style. Existing evaluations are mostly conducted within a single source, leaving open whether common LLM adaptation strategies remain effective under source shift. We reframe climate disclosure classification as a cross-source adaptation problem and study three widely used adaptation strategies{---} definitions, examples, and fine-tuning{---}across eleven open- and closed-source LLMs, using two corpora that share the same label space but come from different sources. We find that all strategies bring positive cross-source gains on average, but the strongest in-source strategies are not the strongest cross-source ones: similarity-based retrieval and LoRA fine-tuning gain most in-source but lose most of that advantage under source shift; randomly selected few-shot examples, a weaker in-source baseline, retain their advantage more reliably; definitions transfer most consistently, though only when their granularity matches the target text. Across these strategies, when the source changes, simpler is often safer."
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<abstract>Climate disclosure classification is a fundamental task for analysing corporate climate disclosures, yet such disclosures appear in many different sources—annual reports, press releases, and earnings calls—that differ in length, purpose, and writing style. Existing evaluations are mostly conducted within a single source, leaving open whether common LLM adaptation strategies remain effective under source shift. We reframe climate disclosure classification as a cross-source adaptation problem and study three widely used adaptation strategies— definitions, examples, and fine-tuning—across eleven open- and closed-source LLMs, using two corpora that share the same label space but come from different sources. We find that all strategies bring positive cross-source gains on average, but the strongest in-source strategies are not the strongest cross-source ones: similarity-based retrieval and LoRA fine-tuning gain most in-source but lose most of that advantage under source shift; randomly selected few-shot examples, a weaker in-source baseline, retain their advantage more reliably; definitions transfer most consistently, though only when their granularity matches the target text. Across these strategies, when the source changes, simpler is often safer.</abstract>
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%0 Conference Proceedings
%T What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification
%A Li, Guosheng
%A Ren, Fenghui
%A Liu, Bin
%A Yu, Chuan
%A Ji, Kaiying
%A Yue, Lin
%A Shen, Jun
%A Qian, Sasa
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F li-etal-2026-transfers
%X Climate disclosure classification is a fundamental task for analysing corporate climate disclosures, yet such disclosures appear in many different sources—annual reports, press releases, and earnings calls—that differ in length, purpose, and writing style. Existing evaluations are mostly conducted within a single source, leaving open whether common LLM adaptation strategies remain effective under source shift. We reframe climate disclosure classification as a cross-source adaptation problem and study three widely used adaptation strategies— definitions, examples, and fine-tuning—across eleven open- and closed-source LLMs, using two corpora that share the same label space but come from different sources. We find that all strategies bring positive cross-source gains on average, but the strongest in-source strategies are not the strongest cross-source ones: similarity-based retrieval and LoRA fine-tuning gain most in-source but lose most of that advantage under source shift; randomly selected few-shot examples, a weaker in-source baseline, retain their advantage more reliably; definitions transfer most consistently, though only when their granularity matches the target text. Across these strategies, when the source changes, simpler is often safer.
%U https://aclanthology.org/2026.findings-emnlp.1825/
%P 38975-38989
Markdown (Informal)
[What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification](https://aclanthology.org/2026.findings-emnlp.1825/) (Li et al., Findings 2026)
ACL
- Guosheng Li, Fenghui Ren, Bin Liu, Chuan Yu, Kaiying Ji, Lin Yue, Jun Shen, and Sasa Qian. 2026. What Transfers Under Source Shift? Definitions, Examples, and Fine-Tuning for Climate Disclosure Classification. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 38975–38989, Budapest, Hungary. Association for Computational Linguistics.