@inproceedings{zhang-etal-2026-beyond-task,
title = "Beyond Task-Level Context: Class-Conditional Context Vectors for Implicit In-Context Learning",
author = "Zhang, Jianxin and
Hu, Yilu and
Liu, Teng and
Guo, Pei and
Li, Juntao",
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.1527/",
pages = "30543--30566",
ISBN = "979-8-89176-395-1",
abstract = "Implicit In-Context Learning compresses demonstration examples into a single context vector and injects it into the model{'}s activation space, achieving few-shot-level performance at near zero-shot inference cost. However, existing approaches typically aggregate demonstrations from all classes into a shared, task-level context vector, capturing global task information but without explicitly preserving fine-grained, class-conditional semantic distinctions. In this work, we propose Class-Conditional Context Vectors (C{3}V), a simple yet effective extension to implicit in-context learning that explicitly models class-specific contextual information by constructing separate context vectors for each class, without relying on explicit prompts. These class-conditional context vectors are additively injected into the model{'}s residual streams in a single forward pass, enabling contextual contributions to be modulated in a class-aware manner while keeping the backbone frozen. We evaluate C{3}V on multiple text classification benchmarks across several families of large language models. Experimental results demonstrate that C{3}V consistently outperforms task-level context vector baselines, and achieves higher average accuracy than conventional few-shot learning on most models."
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<abstract>Implicit In-Context Learning compresses demonstration examples into a single context vector and injects it into the model’s activation space, achieving few-shot-level performance at near zero-shot inference cost. However, existing approaches typically aggregate demonstrations from all classes into a shared, task-level context vector, capturing global task information but without explicitly preserving fine-grained, class-conditional semantic distinctions. In this work, we propose Class-Conditional Context Vectors (C3V), a simple yet effective extension to implicit in-context learning that explicitly models class-specific contextual information by constructing separate context vectors for each class, without relying on explicit prompts. These class-conditional context vectors are additively injected into the model’s residual streams in a single forward pass, enabling contextual contributions to be modulated in a class-aware manner while keeping the backbone frozen. We evaluate C3V on multiple text classification benchmarks across several families of large language models. Experimental results demonstrate that C3V consistently outperforms task-level context vector baselines, and achieves higher average accuracy than conventional few-shot learning on most models.</abstract>
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%0 Conference Proceedings
%T Beyond Task-Level Context: Class-Conditional Context Vectors for Implicit In-Context Learning
%A Zhang, Jianxin
%A Hu, Yilu
%A Liu, Teng
%A Guo, Pei
%A Li, Juntao
%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 zhang-etal-2026-beyond-task
%X Implicit In-Context Learning compresses demonstration examples into a single context vector and injects it into the model’s activation space, achieving few-shot-level performance at near zero-shot inference cost. However, existing approaches typically aggregate demonstrations from all classes into a shared, task-level context vector, capturing global task information but without explicitly preserving fine-grained, class-conditional semantic distinctions. In this work, we propose Class-Conditional Context Vectors (C3V), a simple yet effective extension to implicit in-context learning that explicitly models class-specific contextual information by constructing separate context vectors for each class, without relying on explicit prompts. These class-conditional context vectors are additively injected into the model’s residual streams in a single forward pass, enabling contextual contributions to be modulated in a class-aware manner while keeping the backbone frozen. We evaluate C3V on multiple text classification benchmarks across several families of large language models. Experimental results demonstrate that C3V consistently outperforms task-level context vector baselines, and achieves higher average accuracy than conventional few-shot learning on most models.
%U https://aclanthology.org/2026.findings-acl.1527/
%P 30543-30566
Markdown (Informal)
[Beyond Task-Level Context: Class-Conditional Context Vectors for Implicit In-Context Learning](https://aclanthology.org/2026.findings-acl.1527/) (Zhang et al., Findings 2026)
ACL