# A Three-Pronged Approach to Cross-Lingual Adaptation with Multilingual LLMs

arXiv: https://arxiv.org/abs/2406.17377 · HTML: https://mlsanity.com/paper/2406.17377/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 35 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers](https://mlsanity.com/paper/2102.01454/) (2021)
- [Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca](https://mlsanity.com/paper/2304.08177/) (2023)
- [SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings](https://mlsanity.com/paper/2004.08728/) (2020)
- [Continual Pre-Training of Large Language Models: How to (re)warm your model?](https://mlsanity.com/paper/2308.04014/) (2023)
- [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://mlsanity.com/paper/2104.05596/) (2021)
- [Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment](https://mlsanity.com/paper/2305.05940/) (2023)
- [Contrastive Search Is What You Need For Neural Text Generation](https://mlsanity.com/paper/2210.14140/) (2022)
- [OpenICL: An Open-Source Framework for In-context Learning](https://mlsanity.com/paper/2303.02913/) (2023)
- [AfroBench: How Good are Large Language Models on African Languages?](https://mlsanity.com/paper/2311.07978/) (2023)
- [Airavata: Introducing Hindi Instruction-tuned LLM](https://mlsanity.com/paper/2401.15006/) (2024)
- [Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages](https://mlsanity.com/paper/2212.10168/) (2022)
- [Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?](https://mlsanity.com/paper/2403.01929/) (2024)
- [ZGUL: Zero-shot Generalization to Unseen Languages using Multi-source Ensembling of Language Adapters](https://mlsanity.com/paper/2310.16393/) (2023)
- [Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition Errors](https://mlsanity.com/paper/2310.16609/) (2023)

## Built on by (most cited first, 6 of 6)

- [Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training](https://mlsanity.com/paper/2504.20484/) (2025)
- [Can LLMs Simulate L2-English Dialogue? An Information-Theoretic Analysis of L1-Dependent Biases](https://mlsanity.com/paper/2502.14507/) (2025)
- [XL-Suite: Cross-Lingual Synthetic Training and Evaluation Data for Open-Ended Generation](https://mlsanity.com/paper/2503.22973/) (2025)
- [Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model](https://mlsanity.com/paper/2410.16006/) (2024)
- [Toward Robust Multilingual Adaptation of LLMs for Low-Resource Languages](https://mlsanity.com/paper/2510.14466/) (2025)
- [Lost in Reordering: Structural Sensitivity of Multilingual LLMs under Semantics-Preserving Perturbations](https://mlsanity.com/paper/2609.03511/) (2026)

## Abstract (arXiv)

Low-resource languages, by its very definition, tend to be under represented in the pre-training corpora of Large Language Models. In this work, we investigate three low-resource cross-lingual approaches that enable an LLM adapt to tasks in previously unseen languages. Llama-2 is an LLM where Indic languages, among many other language families, contribute to less than 0.005% of the total 2 trillion token pre-training corpora. In this work, we experiment with the English-dominated Llama-2 for cross-lingual transfer to three Indic languages, Bengali, Hindi, and Tamil as target languages. We study three approaches for cross-lingual transfer, under ICL and fine-tuning. One, we find that adding additional supervisory signals via a dominant language in the LLM, leads to improvements, both under in-context learning and fine-tuning. Two, adapting the target languages to word reordering may be beneficial under ICL, but its impact diminishes with fine tuning. Finally, continued pre-training in one low-resource language can improve model performance for other related low-resource languages.