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

By PulseAugur Editorial · [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

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 →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

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

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The cluster contains multiple arXiv papers detailing novel research in language model instruction tuning and data selection.
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COVERAGE [4]

  1. arXiv cs.CL TIER_1 English(EN) · 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 English(EN) · 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 English(EN) · 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 English(EN) · 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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