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LLMs Are Few-Shot In-Context Low-Resource Language Learners

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arxiv 2403.16512 v5 pith:AGMG5H5O submitted 2024-03-25 cs.CL cs.AI

Samuel Cahyawijaya , Holy Lovenia , Pascale Fung This is my paper · ORCID
classification cs.CLcs.AI
keywords low-resourcelanguagesin-contextlanguagellmshigh-resourceinformationonly
verification ladder T0 review T1 audit T2 compute T3 formal
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In-context learning (ICL) empowers large language models (LLMs) to perform diverse tasks in underrepresented languages using only short in-context information, offering a crucial avenue for narrowing the gap between high-resource and low-resource languages. Nonetheless, there is only a handful of works explored ICL for low-resource languages with most of them focusing on relatively high-resource languages, such as French and Spanish. In this work, we extensively study ICL and its cross-lingual variation (X-ICL) on 25 low-resource and 7 relatively higher-resource languages. Our study not only assesses the effectiveness of ICL with LLMs in low-resource languages but also identifies the shortcomings of in-context label alignment, and introduces a more effective alternative: query alignment. Moreover, we provide valuable insights into various facets of ICL for low-resource languages. Our study concludes the significance of few-shot in-context information on enhancing the low-resource understanding quality of LLMs through semantically relevant information by closing the language gap in the target language and aligning the semantics between the targeted low-resource and the high-resource language that the model is proficient in. Our work highlights the importance of advancing ICL research, particularly for low-resource languages. Our code is publicly released at https://github.com/SamuelCahyawijaya/in-context-alignment

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Cited by 4 Pith papers

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  1. Test-Time Scaling via Error Localization

    cs.LG 2026-07 conditional novelty 6.0 of 10

    TTEL uses feedback-induced token probability drops to localize the first error in a failed reasoning trace and branch a new generation from that prefix, improving pass@k per token on coding and math benchmarks.

  2. Meta-Learning Preferences for Multilingual LLM Alignment

    cs.CL 2026-07 conditional novelty 6.0 of 10

    Meta-learning a shared initialization on multilingual preference data lets LLMs align to a new language from ~100 preference samples, with up to 28% win-rate gains over baselines.

  3. Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI

    cs.LG 2025-06 conditional novelty 5.0 of 10

    An in-context LLM framework that produces dual expert and non-expert explanations, evaluated on well-being clustering with a user study and LIME-alignment metrics.

  4. TALL -- A Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages

    cs.CL 2025-06 reject novelty 5.0 of 10

    A trainable pipeline of translation models and a frozen LLM improves Hebrew last-word prediction accuracy to 5.59%, about twice the best baseline.

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