Exploring In-context Example Generation for Machine Translation

• URL: http://arxiv.org/abs/2506.00507v1
• Date: Sat, 31 May 2025 11:00:49 GMT
• Title: Exploring In-context Example Generation for Machine Translation
• Authors: Dohyun Lee, Seungil Chad Lee, Chanwoo Yang, Yujin Baek, Jaegul Choo,
• Abstract summary: Large language models (LLMs) have demonstrated strong performance across various tasks, leveraging their exceptional in-context learning ability with only a few examples.<n>The selection of optimal in-context examples has been actively studied in the field of machine translation.<n>This paper explores the research direction of in-context example generation for machine translation.
• Score: 31.452440230088577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have demonstrated strong performance across various tasks, leveraging their exceptional in-context learning ability with only a few examples. Accordingly, the selection of optimal in-context examples has been actively studied in the field of machine translation. However, these studies presuppose the presence of a demonstration pool with human-annotated pairs, making them less applicable to low-resource languages where such an assumption is challenging to meet. To overcome this limitation, this paper explores the research direction of in-context example generation for machine translation. Specifically, we propose Demonstration Augmentation for Translation (DAT), a simple yet effective approach that generates example pairs without relying on any external resources. This method builds upon two prior criteria, relevance and diversity, which have been highlighted in previous work as key factors for in-context example selection. Through experiments and analysis on low-resource languages where human-annotated pairs are scarce, we show that DAT achieves superior translation quality compared to the baselines. Furthermore, we investigate the potential of progressively accumulating generated pairs during test time to build and reuse a demonstration pool. Our implementation is publicly available at https://github.com/aiclaudev/DAT.

Related papers

• PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks [57.86928556668849]
  Large Language Models (LLMs) have recently demonstrated impressive few-shot learning capabilities through in-context learning (ICL)<n>ICL performance is highly dependent on the choice of few-shot demonstrations, making the selection of the most optimal examples a persistent research challenge.<n>In this work, we propose PromptRefine, a novel Alternating Minimization approach for example selection that improves ICL performance on low-resource Indic languages.
  arXiv  Detail & Related papers  (2024-12-07T17:51:31Z)
• P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs [84.24644520272835]
  We introduce P-MMEval, a large-scale benchmark covering effective fundamental and capability-specialized datasets.<n>P-MMEval delivers consistent language coverage across various datasets and provides parallel samples.<n>We conduct extensive experiments on representative multilingual model series to compare performances across models and tasks.
  arXiv  Detail & Related papers  (2024-11-14T01:29:36Z)
• Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs [16.98133269527045]
  We propose an unsupervised approach to mine in-context examples for machine translation (MT)<n>We introduce a filtering criterion to select the optimal in-context examples from a pool of unsupervised parallel sentences.<n>Our findings demonstrate the effectiveness of our unsupervised approach in mining in-context examples for MT.
  arXiv  Detail & Related papers  (2024-10-14T18:47:04Z)
• In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation [20.704153242284114]
  We focus on machine translation (MT), a task that has been shown to benefit from in-context translation examples. No systematic studies have been published on how best to select examples, and mixed results have been reported on the usefulness of similarity-based selection. We find that sentence embedding similarity can improve MT, especially for low-resource language directions.
  arXiv  Detail & Related papers  (2024-08-01T09:07:32Z)
• DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase [37.68804898063595]
  In-Context Learning (ICL) combined with pre-trained large language models has achieved promising results on various NLP tasks. We propose textbfData textbfAugmentation for textbfIn-Context textbfLearning (textbfDAIL)
  arXiv  Detail & Related papers  (2023-11-06T18:12:55Z)
• Generative Judge for Evaluating Alignment [84.09815387884753]
  We propose a generative judge with 13B parameters, Auto-J, designed to address these challenges. Our model is trained on user queries and LLM-generated responses under massive real-world scenarios. Experimentally, Auto-J outperforms a series of strong competitors, including both open-source and closed-source models.
  arXiv  Detail & Related papers  (2023-10-09T07:27:15Z)
• Finding Support Examples for In-Context Learning [73.90376920653507]
  We propose LENS, a fiLter-thEN-Search method to tackle this challenge in two stages. First we filter the dataset to obtain informative in-context examples individually. Then we propose diversity-guided example search which iteratively refines and evaluates the selected example permutations.
  arXiv  Detail & Related papers  (2023-02-27T06:32:45Z)
• Prompting Large Language Model for Machine Translation: A Case Study [87.88120385000666]
  We offer a systematic study on prompting strategies for machine translation. We examine factors for prompt template and demonstration example selection. We explore the use of monolingual data and the feasibility of cross-lingual, cross-domain, and sentence-to-document transfer learning.
  arXiv  Detail & Related papers  (2023-01-17T18:32:06Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• In-context Examples Selection for Machine Translation [101.50473468507697]
  Large-scale generative models show an impressive ability to perform a wide range of Natural Language Processing (NLP) tasks using in-context learning. For Machine Translation (MT), these examples are typically randomly sampled from the development dataset with a similar distribution as the evaluation set. We show that the translation quality and the domain of the in-context examples matter and that 1-shot noisy unrelated example can have a catastrophic impact on output quality.
  arXiv  Detail & Related papers  (2022-12-05T17:25:15Z)
• IGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages [87.5457337866383]
  We introduce the Image-Grounded Language Understanding Evaluation benchmark. IGLUE brings together visual question answering, cross-modal retrieval, grounded reasoning, and grounded entailment tasks across 20 diverse languages. We find that translate-test transfer is superior to zero-shot transfer and that few-shot learning is hard to harness for many tasks.
  arXiv  Detail & Related papers  (2022-01-27T18:53:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.