Summary of Lb-kbqa: Large-language-model and Bert Based Knowledge-based Question and Answering System, by Yan Zhao et al.
LB-KBQA: Large-language-model and BERT based Knowledge-Based Question and Answering System
by Yan Zhao, Zhongyun Li, Yushan Pan, Jiaxing Wang, Yihong Wang
First submitted to arxiv on: 5 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 paper proposes a novel Knowledge-Based-Question-and-Answer (KBQA) system that leverages large language models (LLMs) to improve natural language understanding and intent recognition in the financial domain. By integrating LLMs with BERT-based architectures, the LB-KBQA system can detect newly appeared intents and acquire new knowledge, outperforming conventional AI-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new approach to knowledge-based question answering that uses large language models to improve natural language understanding. The method combines the strengths of both LLMs and BERT to recognize intent and answer questions in the financial domain. This breakthrough has the potential to revolutionize how we process and analyze vast amounts of financial data. |
Keywords
» Artificial intelligence » Bert » Language understanding » Question answering
