[論文レビュー] Agent AI with LangGraph: A Modular Framework for Enhancing Machine Translation Using Large Language Models

Jialin Wang, Zhihua Duan|arXiv (Cornell University)|Dec 5, 2024

Natural Language Processing Techniques被引用数 6

ひとこと要約

The paper proposes a modular Agent AI system built on LangGraph to orchestrate multilingual translation agents that leverage LLMs like GPT-4o for improved translation accuracy and scalability.

ABSTRACT

This paper explores the transformative role of Agent AI and LangGraph in advancing the automation and effectiveness of machine translation (MT). Agents are modular components designed to perform specific tasks, such as translating between particular languages, with specializations like TranslateEnAgent, TranslateFrenchAgent, and TranslateJpAgent for English, French, and Japanese translations, respectively. These agents leverage the powerful semantic capabilities of large language models (LLMs), such as GPT-4o, to ensure accurate, contextually relevant translations while maintaining modularity, scalability, and context retention. LangGraph, a graph-based framework built on LangChain, simplifies the creation and management of these agents and their workflows. It supports dynamic state management, enabling agents to maintain dialogue context and automates complex workflows by linking agents and facilitating their collaboration. With flexibility, open-source community support, and seamless integration with LLMs, LangGraph empowers agents to deliver high-quality translations. Together, Agent AI and LangGraph create a cohesive system where LangGraph orchestrates agent interactions, ensuring that user inputs are analyzed, routed, and processed efficiently. Experimental results demonstrate the potential of this system to enhance multilingual translation accuracy and scalability. By highlighting modular design and automated workflows, this paper sets the stage for further innovations in intelligent machine translation services.

研究の動機と目的

• Motivate and enable improved multilingual machine translation through modular agents.
• Demonstrate LangGraph as a workflow orchestration framework for multi-agent translation tasks.
• Showcase integration of LLMs to enhance semantic understanding and context retention in translations.
• Evaluate translation quality and scalability using English-French data as a testbed.

提案手法

• Define language-specific translation agents (TranslateEnAgent, TranslateFrenchAgent, TranslateJpAgent) that call LLM-based translation services.
• Use LangGraph to orchestrate agent interactions with a stateful graph-based workflow.
• Employ an IntentAgent to route requests to the appropriate translation agent based on input language and intent.
• Train a Seq2seq/RNN-based baseline MT model and compare with LLM-enabled translation in the experimental setup.
• Evaluate translations using standard MT metrics such as BLEU across language pairs.

実験結果

リサーチクエスチョン

• RQ1 Can LangGraph-based agent orchestration improve multilingual translation quality and scalability?
• RQ2 How effectively can modular translation agents leveraging LLMs handle English, French, and Japanese translation tasks?
• RQ3 Does an intent-driven routing mechanism improve translation coherence and contextual relevance?

主な発見

• Agent-based LangGraph translation flow can route translation tasks to language-specific agents.
• Experiments show the system supports English–French translation experiments with alignment between source and translated text.
• BLEU-based evaluation indicates suboptimal performance in the baseline Seq2seq setup due to simpler model architecture and limited data.
• LangGraph-enabled agents demonstrate coherent translation processes and potential for practical multilingual translation applications.
• The study highlights scalability and modularity advantages of the agent-based approach for future language services.