Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs

• URL: http://arxiv.org/abs/2410.11006v1
• Date: Mon, 14 Oct 2024 18:47:04 GMT
• Title: Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs
• Authors: Abdellah El Mekki, Muhammad Abdul-Mageed,
• Abstract summary: We propose an unsupervised approach to mine in-context examples for machine translation (MT) We introduce a filtering criterion to select the optimal in-context examples from a pool of unsupervised parallel sentences. Our findings demonstrate the effectiveness of our unsupervised approach in mining in-context examples for MT.
• Score: 16.98133269527045
• License:
• Abstract: Large Language Models (LLMs) have demonstrated impressive performance on a wide range of natural language processing (NLP) tasks, primarily through in-context learning (ICL). In ICL, the LLM is provided with examples that represent a given task such that it learns to generate answers for test inputs. However, access to these in-context examples is not guaranteed especially for low-resource or massively multilingual tasks. In this work, we propose an unsupervised approach to mine in-context examples for machine translation (MT), enabling unsupervised MT (UMT) across different languages. Our approach begins with word-level mining to acquire word translations that are then used to perform sentence-level mining. As the quality of mined parallel pairs may not be optimal due to noise or mistakes, we introduce a filtering criterion to select the optimal in-context examples from a pool of unsupervised parallel sentences. We evaluate our approach using two multilingual LLMs on 288 directions from the FLORES-200 dataset and analyze the impact of various linguistic features on performance. Our findings demonstrate the effectiveness of our unsupervised approach in mining in-context examples for MT, leading to better or comparable translation performance as translation with regular in-context samples (extracted from human-annotated data), while also outperforming the other state-of-the-art UMT methods by an average of $7$ BLEU points.

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