Train Once, Reuse Everywhere: Generalizable Implicit In-Context Learning by Routing Attention
arXiv:2509.22854v2 Announce Type: replace Abstract: Implicit in-context learning (ICL) has newly emerged as a promising paradigm that simulates ICL behaviors in the representation space of large language models (LLMs), aiming to attain few-shot performance at zero-shot cost. However, existing approaches largely rely on injecting shift vectors into residual flows, which are typically constructed from labeled demonstrations or task-specific alignment. Such designs fall short of utilizing the structural mechanisms underlying ICL and suffer from limited generalizability. To address this, we propos
The paper addresses a current limitation in implicit in-context learning (ICL) within large language models (LLMs), which is a key area of development for more efficient and effective AI paradigms.
This development proposes a method to significantly enhance the generalizability and efficiency of ICL in LLMs, allowing for few-shot performance at zero-shot cost, critical for scaling AI applications and reducing dependency on extensive labeling.
The proposed 'routing attention' mechanism moves beyond reliance on shift vectors, fundamentally altering how implicit ICL can be achieved, leading to more robust and less task-specific AI models.
- · AI developers
- · LLM providers
- · SaaS companies leveraging LLMs
- · Companies reliant on extensive data labeling for AI training
- · Older, less generalizable ICL methods
More efficient and generalizable LLMs become viable for a wider range of applications without specific fine-tuning.
Reduced computational costs and time for deploying new AI applications, accelerating AI adoption across sectors.
Enhanced AI capabilities could further compress knowledge work and accelerate the development of sophisticated AI agents.
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Read at arXiv cs.CL