| Challenge: | Large-scale generative models can perform a wide range of NLP tasks using in-context learning. |
| Approach: | They aim to understand the properties of good in-context examples for machine translation in both in-domain and out-of-domain settings. |
| Outcome: | The proposed model outperforms a strong kNN-MT baseline in 2 out of 4 out-of-domain datasets. |
Similar Papers
In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing studies have shown that in-context examples for machine translation are beneficial for high-resource languages. |
| Approach: | They propose to use in-context examples for machine translation (MT) they argue that similarity-based selection can improve MT . |
| Outcome: | The proposed approach improves machine translation (MT) and low-resource languages. |
Revisiting Context Choices for Context-aware Machine Translation (2024.lrec-main)
Copied to clipboard
| Challenge: | Recent work has cast doubt on whether context-aware machine translation models learn useful signals from context or are improvements in automatic evaluation metrics just a side-effect. |
| Approach: | They propose to use separate encoders for source sentence and context as multiple sources for one target sentence to train context-aware machine translation models. |
| Outcome: | The proposed model improves translation quality even with empty lines as context, but the correct context improves it and random out-of-domain context degrades it. |
Topic-guided Example Selection for Domain Adaptation in LLM-based Machine Translation (2024.eacl-srw)
Copied to clipboard
| Challenge: | Current machine translation (MT) systems perform well in domains on which they were trained, but adaptation to unseen domains remains a challenge. |
| Approach: | They propose to use large language models to adapt to unseen domains by in-context example selection. |
| Outcome: | The proposed method outperforms baselines on multilingual out-of-domain tests, though it does not match performance with strong baselines for the in-language setting. |
An Empirical Study of In-context Learning in LLMs for Machine Translation (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies focus on optimizing translation quality, with limited attention to understanding specific aspects of ICL that influence the said quality. |
| Approach: | They conduct the first of its kind, exhaustive study of in-context learning for machine translation (MT) they establish that ICL is primarily example-driven and not instruction-driven . |
| Outcome: | The proposed model is based on examples and not instruction-driven learning. |
Exploring In-context Example Generation for Machine Translation (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated strong performance across various tasks with just a few examples. |
| Approach: | They propose a method that generates in-context example pairs without external resources. |
| Outcome: | The proposed method builds upon two prior criteria, relevance and diversity, which have been highlighted as key factors for in-context example selection. |
Submodular-based In-context Example Selection for LLMs-based Machine Translation (2024.lrec-main)
Copied to clipboard
| Challenge: | Prior studies have focused on the role of well-chosen examples in in-context learning . |
| Approach: | They propose to use multiple translational factors for in-context example selection by using monotone submodular function maximization. |
| Outcome: | The proposed approach outperforms random selection and robust single-factor baselines across various NLP tasks. |
Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu (2025.acl-long)
Copied to clipboard
| Challenge: | In-context machine translation (MT) with large language models can take advantage of linguistic resources such as grammar books and dictionaries. |
| Approach: | They propose to use in-context machine translation (MT) with large language models to take advantage of linguistic resources such as grammar books and dictionaries. |
| Outcome: | The proposed approach can take advantage of dictionaries and grammar books, but its performance is poor for many lowresource languages. |
Analyzing Context Contributions in LLM-based Machine Translation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) have achieved state-of-the-art performance in machine translation . however, the mechanisms by which LLMs use different parts of the input context remain unexplored . |
| Approach: | They propose to analyze how large language models use different parts of the input context . they highlight several key findings: the source part of few-shot examples contributes more than its corresponding targets . |
| Outcome: | The proposed model can leverage in-context learning to perform translation tasks without training . the proposed model is able to perform tasks without being explicitly trained for them . |
CTQScorer: Combining Multiple Features for In-context Example Selection for Machine Translation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models have demonstrated the capability to perform on machine translation when the input is prompted with a few examples. |
| Approach: | They propose a regression model that combine features influencing example selection to maximize translation quality. |
| Outcome: | The proposed model outperforms random selection and strong single-factor baselines on multiple language pairs and language models. |
Effective In-Context Example Selection through Data Compression (2024.findings-acl)
Copied to clipboard
| Challenge: | In-context learning has been validated in large language models, but the mechanism and selection strategy for in-cont example selection lacks systematic and in-depth research. |
| Approach: | They propose a data compression approach to select in-context examples using large language models. |
| Outcome: | The proposed method shows a significant improvement of 5.90% across five real-world datasets using four language models. |