No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs (2026.findings-acl)
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| 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. |
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| 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. |
Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts (2024.acl-long)
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| 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. |
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The language of prompting: What linguistic properties make a prompt successful? (2023.findings-emnlp)
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| Challenge: | Recent studies show that pretraining and instruction-tuned LLMs can achieve impressive performance on a multitude of tasks. |
| Approach: | They propose to use a standard for prompting research to better understand linguistic properties of LLMs. |
| Outcome: | The proposed standard would improve the performance of pre-trained and instruction-tuned LLMs on a multitude of tasks. |
Prompting PaLM for Translation: Assessing Strategies and Performance (2023.acl-long)
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| Challenge: | Large language models trained on multilingual but not parallel text exhibit remarkable ability to translate between languages. |
| Approach: | They investigate the pathways language model which has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
| Outcome: | The pathways language model (PaLM) has demonstrated the strongest machine translation performance among similarly-trained LLMs to date. |
Multilingual Prompting for Improving LLM Generation Diversity (2025.emnlp-main)
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| 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. |
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Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting (2023.findings-emnlp)
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| 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. |
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Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to the imbalanced training corpora. |
| Approach: | They extend the evaluation to real-world user queries and non-English-centric LLMs . they show that translation into English can boost LLM performance on NLP tasks . |
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Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation (2025.naacl-industry)
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| Challenge: | Large language models (LLMs) have significantly advanced autonomous agents, particularly in zero-shot tool usage, also known as function calling. |
| Approach: | They propose to integrate function descriptions into prompt formats and introduce a new Decision Token for conditional prompts. |
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Chain-of-Dictionary Prompting Elicits Translation in Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown surprisingly good performance in multilingual neural machine translation . yet, they struggle with translating low-resource languages. |
| Approach: | They propose a framework that chained multilingual dictionaries to elicit translation abilities for LLMs . they show that CoD can significantly improve LLM translation by evoking more information . |
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Revisiting Automated Prompting: Are We Actually Doing Better? (2023.acl-short)
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| Challenge: | Recent work demonstrates that Large Language Models are great few-shot learners, and prompting significantly increases their performance on a range of downstream tasks. |
| Approach: | They revisit techniques for automated prompting on six different downstream tasks and a larger range of K-shot learning settings. |
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