Summary of Agent Ai with Langgraph: a Modular Framework For Enhancing Machine Translation Using Large Language Models, by Jialin Wang and Zhihua Duan
Agent AI with LangGraph: A Modular Framework for Enhancing Machine Translation Using Large Language Models
by Jialin Wang, Zhihua Duan
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to machine translation using Agent AI and LangGraph. The agents are designed to perform specific tasks, such as translating between languages like English, French, and Japanese. Leveraging large language models (LLMs) like GPT-4o, the agents ensure accurate translations while retaining context and scalability. This architecture is expected to improve the automation and effectiveness of machine translation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make machines better at understanding different languages. It creates special “agents” that can translate between languages like English, French, or Japanese. These agents use really powerful language models to get it right while keeping track of what’s being said. This is important because it makes machines better at translating, which has many practical uses. |
Keywords
» Artificial intelligence » Gpt » Translation
