Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval
arXiv:2606.00014v1 Announce Type: new Abstract: Although studies have demonstrated that Large Language Models (LLMs) can perform well on Out-of-Distribution (OOD) tasks, their advantage tends to diminish as the distribution shift becomes more severe. Consequently, researchers aim to retrieve distributionally similar and informative demonstrations from the available source domain to boost the inference capabilities of LLMs. However, in practical scenarios where the target domain is inaccessible, evaluating the unknown distribution is challenging, which indirectly impacts the quality of the sele
The continuous drive to improve Large Language Model (LLM) performance, particularly in out-of-distribution scenarios, is a key focus, with research emerging to address current limitations.
Improving LLM robustness in diverse and novel tasks is crucial for real-world applications and expands the utility of AI systems beyond narrow, pre-trained domains.
The ability to effectively retrieve demonstrations from source domains without direct access to target distribution significantly enhances the adaptability and generalization of LLMs.
- · AI developers
- · Enterprises adopting LLMs
- · AI-driven automation
- · Legacy AI systems with limited adaptability
Enhanced performance and reliability of LLMs in diverse and previously unseen applications.
Reduced need for extensive re-training or fine-tuning of LLMs for novel tasks, accelerating AI deployment.
Broader adoption of AI agents across various industries, leading to more sophisticated automation workflows.
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