Prompting Large Language Model for Machine Translation: A Case Study (2301.07069v2)

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.

Prompting LLMs for Machine Translation: A Comprehensive Analysis

The paper "Prompting LLM for Machine Translation: A Case Study" provides an exhaustive analysis of using prompting strategies with LLMs specifically for machine translation (MT). Prompting has been a successful approach in various NLP tasks, allowing models to achieve significant results with minimal or no supervised training. However, its application to MT remains under-explored, and this work addresses this gap by systematically examining various aspects of prompting in this context using the GLM-130B model as a testbed.

Key Findings and Contributions

1. Prompt Template and Example Selection: The paper highlights the substantial impact of the prompt template and the selection of demonstration examples on translation quality. It was found that a simplistic English template specifying the source and target languages generally yielded the best performance. However, the relevance and quality of demonstration examples significantly influence outcomes, though the correlation strength is not consistently robust enough to guarantee optimal results always. This indicates that while features like semantic similarity and length of demonstration have some correlation with performance, the inconsistency suggests these should be treated as part of more complex selection methodologies.
2. Utilization of Monolingual Data: Unlike typical in-context learning approaches in classification tasks, prompting for MT requires maintaining the integrity of source-target mappings. Directly using monolingual data in prompts generally harms translation quality. Nonetheless, constructing pseudo-parallel data via zero-shot prompting for back-/forward-translation offers a valuable method for augmenting data effectively while preserving source-target relationships.
3. Transfer Learning for Prompting: The paper investigates the transferability of demonstration examples across domains, language pairs, and translation granularities (sentence-to-document). Findings suggest transfer learning can be beneficial, offering an advantage over zero-shot techniques. However, the optimality of demonstrations does not generalize well across different settings, indicating that specific adaptations may be necessary for varied scenarios.
4. Challenges and Issues: Despite these gains, prompting for MT is still challenged by several issues such as off-topic generation, prompt traps, and the inability to generalize translation capabilities well across non-pretrained languages, as evidenced by poor direct translation between German and Chinese without leveraging English as a pivot language.

Practical and Theoretical Implications

Practically, this research underscores the potential effectiveness of few-shot prompting strategies and demonstrates that while leveraging LLMs in MT opens up new possibilities, it also requires careful attention to the nuances of language pairing and prompt structuring. Theoretically, the paper provides insights into the complexities of encoding linguistic mappings within LLM prompts, emphasizing the intricate balance between prompt design and the intrinsic biases and abilities of pre-trained models.

Future Directions

The findings prompt further exploration into more sophisticated example selection strategies and adaptive prompting techniques that accommodate the variability and complexity inherent in MT tasks. Given the limitations observed in language pair translations not centered around English, future research could focus on integrating multilingual considerations and potentially refining pretraining processes to enhance direct cross-lingual capabilities.

In conclusion, the paper presents a detailed analysis of prompting strategies for MT, offering tangible strategies to enhance translation quality using LLMs while also recognizing the challenges that need to be addressed as the potential for employing such models in real-world multilingual applications continues to expand.