# Crosslingual Retrieval Augmented In-context Learning for Bangla

arXiv: https://arxiv.org/abs/2311.00587 · HTML: https://mlsanity.com/paper/2311.00587/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Generation and context integration, one of 15 directions in Retrieval-augmented generation.
- Builds on 25 of its 50 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Placed in

- Retrieval-augmented generation / [Generation and context integration](https://mlsanity.com/rag/generation-and-context-integration/)

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [MEGA: Multilingual Evaluation of Generative AI](https://mlsanity.com/paper/2303.12528/) (2023)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla](https://mlsanity.com/paper/2101.00204/) (2021)
- [Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment](https://mlsanity.com/paper/2305.05940/) (2023)
- [BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer](https://mlsanity.com/paper/2305.14857/) (2023)
- [Discrete and Soft Prompting for Multilingual Models](https://mlsanity.com/paper/2109.03630/) (2021)
- [Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network](https://mlsanity.com/paper/2004.07807/) (2020)
- [Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt](https://mlsanity.com/paper/2202.11451/) (2022)
- [Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis](https://mlsanity.com/paper/2308.10783/) (2023)
- [Semantic-Oriented Unlabeled Priming for Large-Scale Language Models](https://mlsanity.com/paper/2202.06133/) (2022)
- [SentiGOLD: A Large Bangla Gold Standard Multi-Domain Sentiment Analysis Dataset and its Evaluation](https://mlsanity.com/paper/2306.06147/) (2023)
- [Supervised learning Methods for Bangla Web Document Categorization](https://mlsanity.com/paper/1410.2045/) (2014)

## Built on by (most cited first, 7 of 7)

- [Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers](https://mlsanity.com/paper/2404.04925/) (2024)
- [Manipulating Large Language Models to Increase Product Visibility](https://mlsanity.com/paper/2404.07981/) (2024)
- [Exploring the Role of Transliteration in In-Context Learning for Low-resource Languages Written in Non-Latin Scripts](https://mlsanity.com/paper/2407.02320/) (2024)
- [Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models](https://mlsanity.com/paper/2505.16538/) (2025)
- [LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval](https://mlsanity.com/paper/2504.16121/) (2025)
- [The Virtuous Cycle: AI-Powered Vector Search and Vector Search-Augmented AI](https://mlsanity.com/paper/2603.09347/) (2026)
- [When Less Is Enough: Context Selection and Prompting Strategies for Bengali News Headline Generation](https://mlsanity.com/paper/2608.15879/) (2026)

## Abstract (arXiv)

The promise of Large Language Models (LLMs) in Natural Language Processing has often been overshadowed by their limited performance in low-resource languages such as Bangla. To address this, our paper presents a pioneering approach that utilizes cross-lingual retrieval augmented in-context learning. By strategically sourcing semantically similar prompts from high-resource language, we enable multilingual pretrained language models (MPLMs), especially the generative model BLOOMZ, to successfully boost performance on Bangla tasks. Our extensive evaluation highlights that the cross-lingual retrieval augmented prompts bring steady improvements to MPLMs over the zero-shot performance.