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New framework models retrieval-based in-context learning as domain adaptation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework for in-context retrieval (ICR), a method of in-context learning where demonstrations are selected from a database based on their similarity to a query. This framework treats ICR as a domain adaptation problem, analyzing its performance under various distributional shifts between the database and test queries. The study provides theoretical guarantees on the benefits and drawbacks of this approach, with experimental results on synthetic and language tasks supporting the findings. AI

IMPACT This research could lead to more efficient and effective methods for adapting large language models to specific domains without extensive fine-tuning.

RANK_REASON Academic paper detailing a new framework for in-context retrieval. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework models retrieval-based in-context learning as domain adaptation

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Academic paper detailing a new framework for in-context retrieval. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yilun Zhu, Naihao Deng, Yingcong Li, Naichen Shi, Clayton Scott ·

    Retrieval-Based In-Context Learning: A Domain Adaptation Framework

    arXiv:2610.05717v2 Announce Type: replace Abstract: In-context retrieval (ICR) is a retrieval-based form of in-context learning (ICL) in which demonstrations are retrieved from a source database based on similarity to the query, rather than sampled independently. In this work, we…

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