Papers with in-context learning framework

2 papers
RELIC: Enhancing Reward Model Generalization for Low-Resource Indic Languages with Few-Shot Examples (2025.findings-emnlp)

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Challenge: a new reward model for low-resource Indic languages is proposed . a preference-based training approach is prohibitively expensive, authors say .
Approach: a new in-context learning framework is proposed to train a retriever to select in-constext examples from low-resource Indic languages.
Outcome: a new in-context learning framework for reward modeling in low-resource Indic languages is developed . the proposed framework outperforms existing examples on three preference datasets .
TABGEN-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation (2025.findings-acl)

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Challenge: Existing approaches to tabular data generation require fine-tuning, which is computationally expensive.
Approach: They propose a new in-context learning framework to prompt a fixed LLM with in-constitut examples to enhance the in-text learning ability of LLMs for tabular data generation.
Outcome: The proposed framework outperforms random selection strategies on five real-world tabular datasets and reduces error rate by 42.2% on fidelity metric.

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