@inproceedings{wu-etal-2026-i3cl,
title = "{I}$^3${CL}: Restoring Interactive Capabilities to Implicit In-Context Learning",
author = "Wu, Hao and
Shi, Bin and
Sun, Kai and
Zhou, Aoxiang and
Liu, Ziqi and
Zhang, Zhiqiang and
Zhou, Jun and
Zeng, Wenhan and
Liu, Peng and
Dong, Bo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1341/",
pages = "30579--30595",
ISBN = "979-8-89176-459-0",
abstract = "To address the limitations of conventional ICL, recent task-vector-based methods, such as Implicit ICL (I$^2$CL), encode demonstrations into a unified task vector that is injected during inference, yielding promising improvements in efficiency and stability. However, existing methods encode each demonstration in isolation, neglecting the rich interactions between demonstrations and between demonstrations and queries{---}a core capability of standard ICL. This results in task vectors with limited expressiveness. To bridge this gap, we propose Interactive Implicit In-Context Learning (I$^3$CL), a simple yet effective framework that restores these essential interactive capabilities. I$^3$CL introduces only two lightweight attention-based modules, preserving the efficiency advantages of I$^2$CL while delivering significant performance improvements. Experiments across nine diverse tasks using four LLMs show that I$^3$CL achieves an average performance improvement of 3.54{\%} over prior task-vector-based baselines. Comprehensive analyses on efficiency, scalability and task vector properties further confirm the superiority of our approach."
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<abstract>To address the limitations of conventional ICL, recent task-vector-based methods, such as Implicit ICL (I²CL), encode demonstrations into a unified task vector that is injected during inference, yielding promising improvements in efficiency and stability. However, existing methods encode each demonstration in isolation, neglecting the rich interactions between demonstrations and between demonstrations and queries—a core capability of standard ICL. This results in task vectors with limited expressiveness. To bridge this gap, we propose Interactive Implicit In-Context Learning (I³CL), a simple yet effective framework that restores these essential interactive capabilities. I³CL introduces only two lightweight attention-based modules, preserving the efficiency advantages of I²CL while delivering significant performance improvements. Experiments across nine diverse tasks using four LLMs show that I³CL achieves an average performance improvement of 3.54% over prior task-vector-based baselines. Comprehensive analyses on efficiency, scalability and task vector properties further confirm the superiority of our approach.</abstract>
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%0 Conference Proceedings
%T I³CL: Restoring Interactive Capabilities to Implicit In-Context Learning
%A Wu, Hao
%A Shi, Bin
%A Sun, Kai
%A Zhou, Aoxiang
%A Liu, Ziqi
%A Zhang, Zhiqiang
%A Zhou, Jun
%A Zeng, Wenhan
%A Liu, Peng
%A Dong, Bo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wu-etal-2026-i3cl
%X To address the limitations of conventional ICL, recent task-vector-based methods, such as Implicit ICL (I²CL), encode demonstrations into a unified task vector that is injected during inference, yielding promising improvements in efficiency and stability. However, existing methods encode each demonstration in isolation, neglecting the rich interactions between demonstrations and between demonstrations and queries—a core capability of standard ICL. This results in task vectors with limited expressiveness. To bridge this gap, we propose Interactive Implicit In-Context Learning (I³CL), a simple yet effective framework that restores these essential interactive capabilities. I³CL introduces only two lightweight attention-based modules, preserving the efficiency advantages of I²CL while delivering significant performance improvements. Experiments across nine diverse tasks using four LLMs show that I³CL achieves an average performance improvement of 3.54% over prior task-vector-based baselines. Comprehensive analyses on efficiency, scalability and task vector properties further confirm the superiority of our approach.
%U https://aclanthology.org/2026.emnlp-main.1341/
%P 30579-30595
Markdown (Informal)
[I3CL: Restoring Interactive Capabilities to Implicit In-Context Learning](https://aclanthology.org/2026.emnlp-main.1341/) (Wu et al., EMNLP 2026)
ACL
- Hao Wu, Bin Shi, Kai Sun, Aoxiang Zhou, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Wenhan Zeng, Peng Liu, and Bo Dong. 2026. I3CL: Restoring Interactive Capabilities to Implicit In-Context Learning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 30579–30595, Budapest, Hungary. Association for Computational Linguistics.