Yen-Hsiang Wang
2024
Learning-From-Mistakes Prompting for Indigenous Language Translation
You Cheng Liao
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Chen-Jui Yu
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Chi-Yi Lin
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He-Feng Yun
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Yen-Hsiang Wang
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Hsiao-Min Li
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Yao-Chung Fan
Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)
Using large language models, this paper presents techniques to improve extremely low-resourced indigenous language translations. Our approaches are grounded in the use of (1) the presence of a datastore consisting of a limited number of parallel translation examples, (2) the inherent capabilities of LLMs like GPT-3.5, and (3) a word-level translation dictionary. We harness the potential of LLMs and in-context learning techniques in such a setting for using LLM as universal translators for extremely low-resourced languages. Our methodology hinges on utilizing LLMs as language compilers for selected language pairs, hypothesizing that they could internalize syntactic structures to facilitate accurate translation. We introduce three techniques: KNN-Prompting with Retrieved Prompting Context, Chain-of-Thought Prompting, and Learning-from-Mistakes Prompting, with the last method addressing past errors. The evaluation results suggest that, even with limited corpora, LLMs, when paired with proper prompting, can effectively translate extremely low-resource languages.
2023
Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation
Xingdi Yuan
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Tong Wang
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Yen-Hsiang Wang
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Emery Fine
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Rania Abdelghani
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Hélène Sauzéon
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Pierre-Yves Oudeyer
Findings of the Association for Computational Linguistics: ACL 2023
Large Language Models (LLMs) have in recent years demonstrated impressive prowess in natural language generation. A common practice to improve generation diversity is to sample multiple outputs from the model. However, partly due to the inaccessibility of LLMs, there lacks a simple and robust way of selecting the best output from these stochastic samples. As a case study framed in the context of question generation, we propose two prompt-based approaches, namely round-trip and prompt-based score, to selecting high-quality questions from a set of LLM-generated candidates. Our method works without the need to modify the underlying model, nor does it rely on human-annotated references — both of which are realistic constraints for real-world deployment of LLMs. With automatic as well as human evaluations, we empirically demonstrate that our approach can effectively select questions of higher qualities than greedy generation.
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Co-authors
- Xingdi Yuan 1
- Tong Wang 1
- Emery Fine 1
- Rania Abdelghani 1
- Hélène Sauzéon 1
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