@inproceedings{liu-etal-2026-bcl,
title = "{BCL}: {B}ayesian In-Context Learning Framework for Information Extraction",
author = "Liu, Haoliang and
Cai, Chengkun and
Zhao, Xu and
Zhu, Han and
Huang, Shizhou and
Zhang, Xinglin and
Chen, Tao and
Hwang, Jenq-Neng and
Huaping, Zhang and
Li, Lei",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1560/",
pages = "31172--31189",
ISBN = "979-8-89176-395-1",
abstract = "Existing information extraction (IE) tasks increasingly adopt in-context learning (ICL) with large language models. However, current approaches either show inconsistent performance across model scales or lack systematic optimization and generalizability. Building on this, we propose BCL-IE (Bayesian In-Context Learning Framework for Information Extraction), the first optimization framework that uses particle filtering with Bayesian updates to systematically refine label representations across IE tasks. Through four steps{---}initialization, observation, weight update, and resampling, BCL-IE generalizes to both sequence labeling and relation classification paradigms. Extensive experiments demonstrate substantial improvements over existing approaches (up to 30{\%}), achieving prior performance while other methods either fail to generalize or show limited effectiveness."
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<abstract>Existing information extraction (IE) tasks increasingly adopt in-context learning (ICL) with large language models. However, current approaches either show inconsistent performance across model scales or lack systematic optimization and generalizability. Building on this, we propose BCL-IE (Bayesian In-Context Learning Framework for Information Extraction), the first optimization framework that uses particle filtering with Bayesian updates to systematically refine label representations across IE tasks. Through four steps—initialization, observation, weight update, and resampling, BCL-IE generalizes to both sequence labeling and relation classification paradigms. Extensive experiments demonstrate substantial improvements over existing approaches (up to 30%), achieving prior performance while other methods either fail to generalize or show limited effectiveness.</abstract>
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%0 Conference Proceedings
%T BCL: Bayesian In-Context Learning Framework for Information Extraction
%A Liu, Haoliang
%A Cai, Chengkun
%A Zhao, Xu
%A Zhu, Han
%A Huang, Shizhou
%A Zhang, Xinglin
%A Chen, Tao
%A Hwang, Jenq-Neng
%A Huaping, Zhang
%A Li, Lei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liu-etal-2026-bcl
%X Existing information extraction (IE) tasks increasingly adopt in-context learning (ICL) with large language models. However, current approaches either show inconsistent performance across model scales or lack systematic optimization and generalizability. Building on this, we propose BCL-IE (Bayesian In-Context Learning Framework for Information Extraction), the first optimization framework that uses particle filtering with Bayesian updates to systematically refine label representations across IE tasks. Through four steps—initialization, observation, weight update, and resampling, BCL-IE generalizes to both sequence labeling and relation classification paradigms. Extensive experiments demonstrate substantial improvements over existing approaches (up to 30%), achieving prior performance while other methods either fail to generalize or show limited effectiveness.
%U https://aclanthology.org/2026.findings-acl.1560/
%P 31172-31189
Markdown (Informal)
[BCL: Bayesian In-Context Learning Framework for Information Extraction](https://aclanthology.org/2026.findings-acl.1560/) (Liu et al., Findings 2026)
ACL
- Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, and Lei Li. 2026. BCL: Bayesian In-Context Learning Framework for Information Extraction. In Findings of the Association for Computational Linguistics: ACL 2026, pages 31172–31189, San Diego, California, United States. Association for Computational Linguistics.