Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts (2024.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) are known to perform tasks by simply observing few exemplars, but performance among under-represented languages falls behind due to pre-training data imbalance. |
| Approach: | They propose to assemble synthetic exemplars from high-resource languages to prompt LLMs to translate from any language into English and use them to create intra-lingual exemplar models to perform tasks in target languages. |
| Outcome: | The proposed method outperforms supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages. |
Similar Papers
A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. |
| Approach: | They evaluate the performance of large language models and their generation strategies in 11 different languages using 3 NLP tasks and 4 open-source LLMs. |
| Outcome: | The proposed generation strategies and their combinations yield strong results across 11 languages, including several extremely low-resource ones. |
No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing studies show that translation-based prompting is not universally optimal for multilingual LLMs. |
| Approach: | They evaluate translation-based prompting across ten languages and four benchmarks . they propose a lightweight classifier that predicts whether native or translation- based prompts are optimal . |
| Outcome: | The proposed classifiers achieve statistically significant improvements over fixed prompting strategies across ten languages and four benchmarks. |
LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings (2024.eacl-tutorials)
Copied to clipboard
| Challenge: | Recent advances in AI can be attributed to the remarkable performance of Large Language Models (LLMs) success of LLMs depends on specific training techniques, such as instruction tuning and prompting . |
| Approach: | They explore the capabilities of Large Language Models (LLMs) in various tasks and languages . they also examine their performance, fine-tuning, instructions tuning, and close vs. open models . |
| Outcome: | The proposed model can be used for speech and multimodal tasks across modalities, languages, and dialects. |
Multilingual Prompting for Improving LLM Generation Diversity (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models lack cultural representation and diversity in their generations . lack of demographic diversity can lead to unfair lack of exposure of artists . |
| Approach: | They propose a prompting method which generates several variations of a base prompt with added cultural and linguistic cues from several cultures, generates responses, and then combines the results. |
| Outcome: | The proposed method outperforms existing diversity-enhancing techniques . it can generate multiple variations of a base prompt with cultural cues from multiple cultures . |
PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) are useful for low-resource scenarios and time-restricted applications. |
| Approach: | They propose a large-scale evaluation tool for large language models that uses prompts . they evaluate 720 prompt templates for open-source LLM-based metrics on MT and summarization datasets a 6.6M evaluations. |
| Outcome: | The proposed model evaluates 720 prompt templates on machine translation and summarization datasets. |
PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) excel in zero-shot document ranking tasks. |
| Approach: | They propose a prompt-based re-ranking method that requires no further training but is only feasible for reranking a handful of candidates due to computational costs. |
| Outcome: | The proposed method can retrieve documents from the entire corpus without training and with a large amount of paired text data. |
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. |
| Approach: | They propose a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. |
| Outcome: | The proposed method improves multilingual capability across languages and covers high-resource and low-resourced languages. |
Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs (2025.findings-naacl)
Copied to clipboard
| Challenge: | Current LLMs are primarily trained on English data but also include data from other languages. |
| Approach: | They propose to use a pre-translation strategy to translate a task prompt into English before inference . they use 'a modular entity' that could be translated into four different languages . |
| Outcome: | The proposed strategies are based on a set of pre-trained data across 35 languages covering both low and high-resource languages. |
Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for zero-shot Relation Extraction (RE) lack detailed, context-specific prompts for understanding various sentences and relations. |
| Approach: | They propose a framework that uses a three-stage diversity approach to prompt LLMs by generating multiple synthetic samples that encapsulate specific relations from scratch. |
| Outcome: | The proposed framework outperforms existing LLM-based zero-shot RE methods on benchmark datasets and shows that it produces high-quality synthetic data that enhances performance. |
PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from related Example Banks (2025.naacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated impressive few-shot learning capabilities through in-context learning. |
| Approach: | They propose a novel Alternating Minimization approach for example selection that improves ICL performance on low-resource Indic languages. |
| Outcome: | The proposed approach outperforms existing frameworks for retrieving examples on low-resource Indic languages. |