Can LLMs translate complex literature better than humans?
(PERHAPS) BEYOND HUMAN TRANSLATION: HARNESSING MULTI-AGENT COLLABORATION FOR TRANSLATING ULTRA-LONG LITERARY TEXTS
August 29, 2024
https://arxiv.org/pdf/2405.11804This paper introduces TRANSAGENTS, a novel multi-agent system for literary translation using Large Language Models (LLMs). It simulates a translation company where different LLM agents with specific roles (editor, translator, localization specialist, etc.) collaborate to translate literary texts.
Key points for LLM-based multi-agent systems:
- Collaboration Strategies: The system utilizes two collaborative strategies: Addition-by-Subtraction, where agents iteratively add and refine content, and Trilateral Collaboration, where agents are responsible for action, critique, and judgment.
- Role Specialization: LLMs are assigned distinct roles with specific prompts to enhance realism and simulate real-world translation processes.
- Evaluation Challenges: The paper proposes two novel evaluation strategies (monolingual human preference and bilingual LLM preference) to address the subjective nature of literary translation and limitations of traditional metrics like BLEU.
- Content Omission Issues: Despite showing promise, the system still exhibits significant content omission issues, highlighting an area for future improvement in LLM-based translation.