Adapting Large Language Models for Document-Level Machine Translation

ACL ARR 2024 December Submission776 Authors

Abstract: Large language models (LLMs) have significantly advanced various natural language processing (NLP) tasks. Recent research indicates that moderately-sized LLMs often outperform larger ones after task-specific fine-tuning. This study focuses on adapting LLMs for document-level machine translation (DocMT) for specific language pairs. We first investigate the impact of prompt strategies on translation performance and then conduct extensive experiments using two fine-tuning methods, three LLM backbones, and 18 translation tasks across nine language pairs. Our results show that specialized models can sometimes surpass GPT-4 in translation performance but still face issues like off-target translation due to error propagation in decoding. We provide an in-depth analysis of these LLMs tailored for DocMT, examining translation errors, discourse phenomena, strategies for training and inference, the data efficiency of parallel documents, recent test set evaluations, and zero-shot crosslingual transfer. Our findings highlight the strengths and limitations of LLM-based DocMT models and provide a foundation for future research.

Paper Type: Long

Research Area: Machine Translation

Research Area Keywords: document-level machine translation, large language models

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English, Arabic, German, French, Italian, Japanese, Korean, Dutch, Romanian, Chinese

Submission Number: 776