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Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment

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arxiv 2305.05940 v3 pith:DGXJXY6J submitted 2023-05-10 cs.CL

Eshaan Tanwar , Subhabrata Dutta , Manish Borthakur , Tanmoy Chakraborty This is my paper · ORCID
classification cs.CL
keywords cross-lingualalignmentin-contextlanguagelargeacrossdifferentinput
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In-context learning (ICL) unfolds as large language models become capable of inferring test labels conditioned on a few labeled samples without any gradient update. ICL-enabled large language models provide a promising step forward toward bypassing recurrent annotation costs in a low-resource setting. Yet, only a handful of past studies have explored ICL in a cross-lingual setting, in which the need for transferring label-knowledge from a high-resource language to a low-resource one is immensely crucial. To bridge the gap, we provide the first in-depth analysis of ICL for cross-lingual text classification. We find that the prevalent mode of selecting random input-label pairs to construct the prompt-context is severely limited in the case of cross-lingual ICL, primarily due to the lack of alignment in the input as well as the output spaces. To mitigate this, we propose a novel prompt construction strategy -- Cross-lingual In-context Source-Target Alignment (X-InSTA). With an injected coherence in the semantics of the input examples and a task-based alignment across the source and target languages, X-InSTA is able to outperform random prompt selection by a large margin across three different tasks using 44 different cross-lingual pairs.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning to Select Visual In-Context Demonstrations

    cs.LG 2026-03 reject novelty 5.0 of 10

    A Dueling-DQN agent selects visual in-context demonstrations and outperforms kNN retrieval on objective regression benchmarks but not on subjective preference tasks, per the paper's main table.

  2. DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer

    cs.AI 2025-07 conditional novelty 5.0 of 10

    DICE dynamically retrieves the most relevant in-context demonstrations at each agent step, and in this preprint it raises exact-match and success-rate scores on HotpotQA, ALFWorld, and Webshop across ReAct, Reflexion,...

  3. Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis

    cs.CL 2025-07 conditional novelty 5.0 of 10

    For Flamingo-style models, increasing the number of in-context examples improves language coherence but degrades visual-text alignment, and similarity-based image retrieval inflates CIDEr scores by encouraging caption...

  4. A Survey on Training-free Alignment of Large Language Models

    cs.CL 2025-08 conditional novelty 4.0 of 10

    A survey that catalogs and categorizes training-free LLM alignment methods into pre-decoding, in-decoding, and post-decoding, with a limited experimental comparison on one model.

  5. Aligning LLMs with Domain Invariant Reward Models

    cs.LG 2025-01 conditional novelty 4.0 of 10

    Applying Wasserstein-distance domain adaptation, a known technique, to reward models lets preference signals learned on labeled source data transfer to unlabeled target domains, with consistent but modest gains across...

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