This repository serves as a comprehensive reading list focused on the application of Large Language Models (LLMs) to Machine Translation (MT). It aims to consolidate and organize the rapidly growing body of research in this domain, providing a valuable resource for researchers, engineers, and practitioners seeking to understand the state-of-the-art techniques, challenges, and advancements in LLM-powered MT. The benefit lies in its curated nature, offering a structured entry point into a complex and fast-moving field.

How It Works

The project is a curated reading list that organizes research on Machine Translation (MT) using Large Language Models (LLMs). It categorizes papers into key areas such as in-context learning, prompting strategies (e.g., Chain-of-Thought, dictionary-based), model pre-training and finetuning, LLM-based evaluation (scorers), post-editing, and interpretability. This structured approach provides a systematic overview of the field's advancements and methodologies.

Quick Start & Requirements

As a reading list, there are no installation or execution requirements. Users need access to academic publications, typically available via platforms like arXiv or publisher websites. Some entries are annotated with { code }, indicating the availability of associated code repositories or implementations for specific research papers.

Highlighted Details

• Comprehensive coverage of LLM applications in MT, from foundational concepts like few-shot learning to advanced techniques in prompting, finetuning, evaluation, and interpretability.
• Aggregates research from leading NLP conferences (ACL, EMNLP, ICML) and pre-print archives, reflecting the cutting edge.
• Many entries link to associated code repositories, enabling practical exploration and reproducibility of research findings.
• Includes diverse research directions, such as LLMs as translation quality evaluators, automated post-editing, and document-level translation strategies.

Maintenance & Community

The list is maintained by Xing Wang and Zhiwei He, with contributions welcomed via pull requests, issues, or email. No specific community channels (like Discord or Slack) or detailed roadmap information are provided in the README snippet.

Licensing & Compatibility

No licensing information is specified in the provided README content. This lack of detail may pose a challenge for users seeking to understand usage rights or compatibility with other projects.

Limitations & Caveats

This repository is a static collection of research pointers and does not offer any executable software or direct tooling. Its value is contingent on the user's ability to access and interpret the cited academic papers. Given the rapid pace of LLM research, the list requires ongoing updates to remain comprehensive and current.