Distributed In-Context Learning under Non-IID Among Clients

ACL ARR 2025 May Submission4258 Authors

Abstract:

Advancements in large language models (LLMs) have shown their effectiveness in multiple complicated natural language reasoning tasks.
A key challenge remains in adapting these models efficiently to new or unfamiliar tasks.
In-context learning (ICL) provides a promising solution for few-shot adaptation by retrieving a set of data points relevant to a query, called in-context examples (ICE), from a training dataset and providing them during the inference as context.
Most existing studies utilize a centralized
training dataset, yet many real-world datasets may be distributed among multiple clients, and remote data retrieval can be associated with costs.
Especially when the client data are non-identical independent distributions (non-IID), retrieving from clients a proper set of ICEs needed for a test query presents critical challenges.
In this paper, we first show that in this challenging setting, test queries will have different preferences among clients because of non-IIDness, and equal contribution often leads to suboptimal performance.
We then introduce a novel approach to tackle the distributed non-IID ICL problem when a data usage budget is present.
The principle is that each client's proper contribution (budget) should be designed according to the preference of each query for that client.
Our approach uses a data-driven manner to allocate a budget for each client, tailored to each test query.
Through extensive empirical studies on diverse datasets, our method demonstrates superior performance relative to competing baselines.

Paper Type: Long

Research Area: Efficient/Low-Resource Methods for NLP

Research Area Keywords: NLP in resource-constrained settings

Contribution Types: Approaches to low-resource settings

Languages Studied: English

Keywords: distributed learning, in-context learning, large language model

Submission Number: 4258