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Researchers explore in-context learning vs. instruction tuning for multilingual models

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 4 sources

Researchers are exploring alternatives to traditional instruction tuning for language models, particularly for smaller and multilingual models. One paper investigates the effectiveness of in-context learning (ICL) for instruction following in non-English languages and across different model sizes, finding that ICL performance degrades in these scenarios. Another study introduces M-DaQ, a framework for creating high-quality, diverse multilingual instruction-tuning datasets that improve model performance across 18 languages. A third paper proposes a data selection method called weighted in-context influence (wICI) to identify effective instruction-tuning data, outperforming existing baselines under data constraints. AI

Summary written by gemini-2.5-flash-lite from 4 sources. How we write summaries →

IMPACT New methods for multilingual instruction tuning and data selection could improve the performance and accessibility of LLMs across diverse languages.

RANK_REASON The cluster contains multiple arXiv papers detailing novel research in language model instruction tuning and data selection.

Read on arXiv cs.CL →

COVERAGE [4]

  1. arXiv cs.CL TIER_1 · David Ponce, Thierry Etchegoyhen ·

    In-context Learning vs. Instruction Tuning: The Case of Small and Multilingual Language Models

    arXiv:2503.01611v3 Announce Type: replace Abstract: Instruction following is a critical ability for Large Language Models to perform downstream tasks. The standard approach to instruction tuning has relied on a specific phase of supervised fine-tuning over curated instruction dat…

  2. arXiv cs.CL TIER_1 · Chunguang Zhao, Yilun Liu, Pufan Zeng, Yuanchang Luo, Shimin Tao, Minggui He, Weibin Meng, Song Xu, Chen Liu, Hongxia Ma, Li Zhang, Boxing Chen, Daimeng Wei ·

    M-DaQ: Retrieving Samples with Multilingual Diversity and Quality for Instruction Fine-Tuning Datasets

    arXiv:2509.15549v2 Announce Type: replace Abstract: Multilingual instruction fine-tuning (IFT) empowers large language models to generalize across diverse linguistic and cultural contexts; however, high-quality, systematically curated multilingual IFT datasets remain scarce. To a…

  3. arXiv cs.CL TIER_1 · Guangzeng Han, Xiaolei Huang ·

    What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective

    arXiv:2604.25132v1 Announce Type: new Abstract: Instruction-tuning datasets often contain substantial redundancy and low-quality samples, necessitating effective data selection methods. We propose an instruction data selection framework based on weighted in-context influence (wIC…

  4. arXiv cs.CL TIER_1 · Xiaolei Huang ·

    What Makes Good Instruction-Tuning Data? An In-Context Learning Perspective

    Instruction-tuning datasets often contain substantial redundancy and low-quality samples, necessitating effective data selection methods. We propose an instruction data selection framework based on weighted in-context influence (wICI), which measures how effectively each candidat…

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