| Challenge: | A NOTAM or NOtice To AirMen is a crucial notification for different stakeholders . writing and understanding these messages puts heavy cognitive load on its end users. |
| Approach: | They propose a framework to translate NOTAMs into English natural language using LLMs . they use context-aware semantic prompting techniques paired with domain-specific rules . |
| Outcome: | The proposed approach improves translation accuracy and clarity using semantic prompting techniques . the proposed framework was evaluated using LLMs of varying sizes and 8 evaluation metrics . |
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Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs (2025.findings-naacl)
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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. |
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. |
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. |
Reasoning for Translation: Comparative Analysis of Chain-of-Thought and Tree-of-Thought Prompting for LLM Translation (2025.acl-srw)
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| Challenge: | Large Language Models (LLMs) have been used for specialized tasks but their application to machine translation has received little attention. |
| Approach: | They evaluate reasoning-based prompting strategies across multiple language pairs and domains and measure their effect on translation quality. |
| Outcome: | The proposed prompting strategies outperform traditional prompting methods across language pairs and domains and achieve improvements of up to 6.4 BLs. |
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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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. |
| Outcome: | The proposed decision token improves function-calling accuracy and relevance detection and a translation pipeline overcomes multilingual limitations. |
Augmenting Large Language Model Translators via Translation Memories (2023.findings-acl)
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Yongyu Mu, Abudurexiti Reheman, Zhiquan Cao, Yuchun Fan, Bei Li, Yinqiao Li, Tong Xiao, Chunliang Zhang, Jingbo Zhu
| Challenge: | Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. |
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| Outcome: | The results are comparable to state-of-the-art NMT systems with bilingual data and are tuned on downstream tasks. |
AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations (2024.findings-emnlp)
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| Challenge: | Existing LLMs are delicate and elusive in prompt words and styles. |
| Approach: | They propose an LLM-acquainted prompting technique that includes proficient "native-speaking" they propose to use in-context learning to prompt LLMs to perform high-performance reasoning . |
| Outcome: | The proposed technique achieves step-wise prompts in zero-shot scenarios while maintaining the prompt quality. |
Prompting Language Models for Linguistic Structure (2023.acl-long)
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| Challenge: | Existing prompting methods can test this hypothesis on autoregressive PLMs. |
| Approach: | They propose a structured prompting approach for linguistic structured prediction tasks that performs zero- and few-shot sequence tagging with autoregressive PLMs. |
| Outcome: | The proposed approach shows that the model can perform few-shot sequence tagging on part-of-speech taging, named entity recognition, and sentence chunking tasks. |
Efficiently Exploring Large Language Models for Document-Level Machine Translation with In-context Learning (2024.findings-acl)
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| Challenge: | Existing studies on sentence-level translation have focused on document level machine translation (DOCMT) document level translation is a complex task different from sentence- level translation. |
| Approach: | They propose a Context-Aware Prompting method which generates more accurate, coherent translations via in-context learning. |
| Outcome: | The proposed method is effective in literary translation tasks and zero pronoun translation tasks. |