上下文学习 vs. 指令微调：小型与多语言语言模型的对比研究

Computer Science > Computation and Language arXiv:2503.01611 (cs) [Submitted on 3 Mar 2025 (v1), last revised 15 Sep 2026 (this version, v4)] Title:In-context Learning vs. Instruction Tuning: The Case of Small and Multilingual Language Models Authors:David Ponce, Thierry Etchegoyhen View a PDF of the paper titled In-context Learning vs. Instruction Tuning: The Case of Small and Multilingual Language Models, by David Ponce and 1 other authors View PDF HTML (experimental) Abstract:Instruction following is a critical ability for Large Language Models to be used directly by humans. This often requires supervised fine-tuning on curated instruction datasets, sometimes complemented with an alignment step. However, in multilingual scenarios, obtaining high-quality data for these stages remains challenging, motivating the exploration of In-Context Learning (ICL) as a possible alternative. In this work, we study whether ICL can serve as a substitute for Instruction Tuning in multilingual language models, while also examining how the comparison changes with model scale. Our results indicate that a gap remains between ICL and Instruction Tuning, motivating further research to reduce it. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2503.01611 [cs.CL] (or arXiv:2503.01611v4 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2503.01611 Focus to learn more arXiv-issued DOI via DataCite Submission history From: David Ponce [view email] [v1] Mon, 3 Mar 2025 14:47:23 UTC (10,294 KB) [v2] Mon, 17 Mar 2025 15:32:53 UTC (10,294 KB) [v3] Thu, 30 Apr 2026 14:25:43 UTC (10,006 KB) [v4] Tue, 15 Sep 2026 06:54:47 UTC (9,616 KB) Full-text links: Access Paper: View a PDF of the paper titled In-context Learning vs. Instruction Tuning: The Case of Small and Multilingual Language Models, by David Ponce and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2025-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)