"Agent AI with LangGraph: A Modular Framework for Enhancing Machine Translation Using Large Language Models"

This paper introduces a modular agent-based framework using LangGraph for machine translation, where specialized agents handle specific language pairs while leveraging LLMs like GPT-4o. The system maintains context and automates complex translation workflows through graph-based state management.

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https://arxiv.org/abs/2412.03801

🤖 Original Problem:

Traditional machine translation methods struggle with context retention and scalability across multiple languages. Current systems lack modularity and efficient state management for handling complex translation workflows.

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🔧 Solution in this Paper:

→ The system implements specialized translation agents (TranslateEnAgent, TranslateFrenchAgent, TranslateJpAgent) for handling specific language pairs.

→ LangGraph orchestrates agent interactions through a graph-based framework, managing state and context across translation tasks.

→ The IntentAgent analyzes input text and selects appropriate translation agents based on language requirements.

→ Each agent leverages LLMs for accurate semantic understanding and contextual translation.

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💡 Key Insights:

→ Modular agent design enables easy addition of new languages and translation paths

→ Graph-based state management preserves context across complex translation workflows

→ Integration of LLMs with specialized agents improves translation accuracy

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📊 Results:

→ BLEU-4 score of 0.052735 for English-French translation

→ Successful implementation of multi-agent translation system supporting English, French, and Japanese

→ Demonstrated context retention across translation tasks