Prompting Large Language Model for Machine Translation: A Case Study

• URL: http://arxiv.org/abs/2301.07069v2
• Date: Wed, 18 Jan 2023 11:30:05 GMT
• Title: Prompting Large Language Model for Machine Translation: A Case Study
• Authors: Biao Zhang, Barry Haddow, Alexandra Birch
• Abstract summary: 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.
• Score: 87.88120385000666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Research on prompting has shown excellent performance with little or even no supervised training across many tasks. However, prompting for machine translation is still under-explored in the literature. We fill this gap by offering a systematic study on prompting strategies for translation, examining various factors for prompt template and demonstration example selection. We further explore the use of monolingual data and the feasibility of cross-lingual, cross-domain, and sentence-to-document transfer learning in prompting. Extensive experiments with GLM-130B (Zeng et al., 2022) as the testbed show that 1) the number and the quality of prompt examples matter, where using suboptimal examples degenerates translation; 2) several features of prompt examples, such as semantic similarity, show significant Spearman correlation with their prompting performance; yet, none of the correlations are strong enough; 3) using pseudo parallel prompt examples constructed from monolingual data via zero-shot prompting could improve translation; and 4) improved performance is achievable by transferring knowledge from prompt examples selected in other settings. We finally provide an analysis on the model outputs and discuss several problems that prompting still suffers from.

Related papers

• The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights [108.40766216456413]
  We propose a question alignment framework to bridge the gap between large language models' English and non-English performance. Experiment results show it can boost multilingual performance across diverse reasoning scenarios, model families, and sizes. We analyze representation space, generated response and data scales, and reveal how question translation training strengthens language alignment within LLMs.
  arXiv  Detail & Related papers  (2024-05-02T14:49:50Z)
• Multilingual Few-Shot Learning via Language Model Retrieval [18.465566186549072]
  Transformer-based language models have achieved remarkable success in few-shot in-context learning. We conduct a study of retrieving semantically similar few-shot samples and using them as the context. We evaluate the proposed method on five natural language understanding datasets related to intent detection, question classification, sentiment analysis, and topic classification.
  arXiv  Detail & Related papers  (2023-06-19T14:27:21Z)
• MetricPrompt: Prompting Model as a Relevance Metric for Few-shot Text Classification [65.51149771074944]
  MetricPrompt eases verbalizer design difficulty by reformulating few-shot text classification task into text pair relevance estimation task. We conduct experiments on three widely used text classification datasets across four few-shot settings. Results show that MetricPrompt outperforms manual verbalizer and other automatic verbalizer design methods across all few-shot settings.
  arXiv  Detail & Related papers  (2023-06-15T06:51:35Z)
• BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer [81.5984433881309]
  We introduce BUFFET, which unifies 15 diverse tasks across 54 languages in a sequence-to-sequence format. BUFFET is designed to establish a rigorous and equitable evaluation framework for few-shot cross-lingual transfer. Our findings reveal significant room for improvement in few-shot in-context cross-lingual transfer.
  arXiv  Detail & Related papers  (2023-05-24T08:06:33Z)
• Decomposed Prompting for Machine Translation Between Related Languages using Large Language Models [55.35106713257871]
  We introduce DecoMT, a novel approach of few-shot prompting that decomposes the translation process into a sequence of word chunk translations. We show that DecoMT outperforms the strong few-shot prompting BLOOM model with an average improvement of 8 chrF++ scores across the examined languages.
  arXiv  Detail & Related papers  (2023-05-22T14:52:47Z)
• In-context Learning as Maintaining Coherency: A Study of On-the-fly Machine Translation Using Large Language Models [15.309754694595322]
  We present a perspective of in-context learning as the desired generation task maintaining coherency with its context. We first investigate randomly sampled prompts across 4 domains, and find that translation performance improves when shown in-domain prompts. In doing so, we demonstrate the efficacy of In-context Machine Translation for on-the-fly adaptation.
  arXiv  Detail & Related papers  (2023-05-05T14:30:20Z)
• Demystifying Prompts in Language Models via Perplexity Estimation [109.59105230163041]
  Performance of a prompt is coupled with the extent to which the model is familiar with the language it contains. We show that the lower the perplexity of the prompt is, the better the prompt is able to perform the task.
  arXiv  Detail & Related papers  (2022-12-08T02:21:47Z)

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.