Summary of Triad: a Framework Leveraging a Multi-role Llm-based Agent to Solve Knowledge Base Question Answering, by Chang Zong et al.
Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering
by Chang Zong, Yuchen Yan, Weiming Lu, Jian Shao, Eliot Huang, Heng Chang, Yueting Zhuang
First submitted to arxiv on: 22 Feb 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 proposed Triad framework is a unified approach for Knowledge Base Question Answering (KBQA) tasks that leverages Large Language Model-based agents with three distinct roles: generalist, decision maker, and advisor. The agent collaborates through four phases to tackle various subtasks, ultimately yielding improved performance on LC-QuAD and YAGO-QA benchmarks, outperforming state-of-the-art systems. This framework addresses the challenges of traditional KBQA methods by utilizing LLM-based agents for mastering diverse subtasks, selecting candidates, and providing answers from knowledge bases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Triad is a new way to help computers answer questions using large collections of information. Right now, there are some good ways to do this, but they can be tricky to use and don’t always work well. The Triad system tries to solve these problems by giving the computer three special jobs: mastering lots of different tasks, picking which answers are best, and actually answering the question using the knowledge it has. This system is tested on several big datasets and does a great job, beating other systems that are currently used. |
Keywords
» Artificial intelligence » Knowledge base » Large language model » Question answering
