T rans A gents: Build Your Translation Company with Language Agents (2024)

Abstract: Multi-agent systems empowered by large language models (LLMs) have demonstrated remarkable capabilities in a wide range of downstream applications. In this work, we introduce TransAgents, a novel multi-agent translation system inspired by human translation companies. TransAgents employs specialized agents—Senior Editor, Junior Editor, Translator, Localization Specialist, and Proofreader—to collaboratively produce translations that are accurate, culturally sensitive, and of high quality. Our system is flexible, allowing users to configure their translation company based on specific needs, and universal, with empirical evidence showing superior performance across various domains compared to state-of-the-art methods. Additionally, TransAgents features a user-friendly interface and offers translations at a cost approximately 80\times cheaper than professional human translation services. Evaluations on literary, legal, and financial test sets demonstrate that TransAgents produces translations preferred by human evaluators, even surpassing human-written references in literary contexts. Our live demo website is available at https://www.transagents.ai/. Our demonstration video is available at https://www.youtube.com/watch?v=p7jIAtF-WKc.

## introduction large language models (llms) have revolutionized the field of natural language processing and artificial intelligence, achieving remarkable progress in various downstream applications @xcite @xcite @xcite @xcite . the superior capabilities of llms also empower a wide range of multi-agent systems @xcite @xcite , enhancing their efficiency and effectiveness in diverse do-* longyue wang is the corresponding author. figure 1 : compared to conventional machine translation (mt) systems that utilize a single mt engine, transagents leverages the collaboration among multiple language agents, each powered by large language models (llms), for translation. mains, including software development @xcite , simulation @xcite @xcite , gaming @xcite , and more. among all the above, one particularly exciting application of multi-agent systems is in the field of machine translation (mt). mt systems, which typically rely on a single model to perform the translation, have achieved considerable success @xcite @xcite . however, these systems often encounter difficulties in accurately handling nuances, context, and idiomatic expressions @xcite . this limitation highlights the need for a superior approach that can handle the subtleties of human language more effectively. consequently, to address the aforementioned limitations of recent mt systems, we draw inspiration from the traditional translation industry's workflow and propose transagents as shown in figure 1 . similar to a human translation company, transagents functions as a virtual multi-agent translation company. it mitigates the challenge of generating high-quality translations by dividing the translation process into several steps and utilizing the collaborative efforts of multiple specialized agents. more specifically, in transagents, each agent is designed to manage specific aspects of the translation process, to produce accurate and natural translations akin to those of human translators. each of our agents plays a specialized role, including senior editor, junior editor, translator, localization specialist, and proofreader. together, these agents replicate the traditional human translation process, delivering translations that are accurate, culturally sensitive, and of high quality. finally, we evaluate transagents alongside other state-ofthe-art translation systems using three test sets from the literary, legal, and financial domains. our experimental results show that, despite lower d-bleu scores, the translations from transagents are significantly more preferred by human evaluators from the target audience compared to other stateof-the-art translation systems. notably, the literary translations provided by transagents are even more preferred than the human-written reference translations. our system is featured by the following characteristics: ## related work large language models large language models (llms) have significantly transformed the field of artificial intelligence. these models are pretrained on extensive text corpora to predict the next word in a sentence, which allows them to understand and generate human-like text @xcite @xcite @xcite . after the initial pretraining phase, llms undergo supervised fine-tuning (sft) or instruction tuning (it). this process helps align the models more closely with human instructions, enhancing their ability to perform specific tasks @xcite @xcite @xcite . recent developments in the field include the use of synthetic datasets generated by llms for fine-tuning. additionally, reinforcement learning from human feedback (rlhf) is employed to further improve the models' performance and reliability @xcite @xcite @xcite . multi-agent systems intelligent agents are designed to understand their environments, make informed decisions, and respond appropriately @xcite . recent multiagent systems utilize collaboration among multiple agents based on llms to tackle complex problems or simulate real-world environments effectively @xcite , such as software development @xcite , multi-robot collaboration @xcite ), text generation @xcite , and simulate societal, economic, and gaming environments @xcite . ## transagents our demo system transagents is implemented as a web application, built using streamlit. 1 the system comprises two main modules: a front-end and a back-end. as illustrated in figure 2 , the frontend module is responsible for accepting user input, including the document to be processed and task configurations (section 3.1). the backend module, on the other hand, handles the translation of the given document by orchestrating the collaborative efforts of our language agents (section 3.2). additionally, we present a step-by-step walkthrough of transagents in section 3.3. ## experiments in this section, we first introduce our experimental setup in section 4.1, followed by presenting the results from both automatic evaluation (section 4.2) and human evaluation (section 4.3). ## case study in this section, we present two case studies from literary test set to demonstrate the superiority of transagents. original text 慕言君仅仅睡了两个时辰，眼睛就睁 开。 reference mu yanjun only slept for four hours before his eyes opened. ## conclusion in this work, we introduce transagents, a novel multi-agent translation system inspired by the traditional human translation process, characterized by its flexibility, universality, user-friendliness, and cost-effectiveness. transagents leverages the collaborative efforts of specialized agents, including a senior editor, junior editor, translator, localization specialist, and proofreader. our experimental results, derived from test sets across literary, legal, and financial domains, highlight the superior performance of transagents. although transagents achieves lower d-bleu scores compared to other state-of-the-art systems, its translations are significantly more preferred by human evaluators. our case study also demonstrates the effectiveness of transagents with regard to translation consistency and culture adaptation.