Summary of Interactive-kbqa: Multi-turn Interactions For Knowledge Base Question Answering with Large Language Models, by Guanming Xiong et al.
Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models
by Guanming Xiong, Junwei Bao, Wen Zhao
First submitted to arxiv on: 23 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 study presents Interactive-KBQA, a framework that generates logical forms by directly interacting with knowledge bases (KBs) using few-shot in-context learning powered by large language models (LLMs). Traditional semantic parsing (SP)-based methods require extensive data annotations, which are costly. To overcome this challenge, the proposed framework develops three generic APIs for KB interaction and devises exemplars to guide LLMs through reasoning processes. The method achieves competitive results on various datasets, including WebQuestionsSP, ComplexWebQuestions, KQA Pro, and MetaQA, with minimal examples (shots). Interactive-KBQA also supports manual intervention, allowing for iterative refinement of LLM outputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to answer questions using computers. It’s like having a super-smart librarian who can understand what you’re asking! Traditional methods require lots of work and data, but this new approach uses special computer models that learn from just a few examples. The researchers created three tools to help these computer models talk to knowledge bases and come up with answers. They tested it on different questions and datasets, and the results were really good! Plus, humans can still help make sure the answers are accurate. |
Keywords
» Artificial intelligence » Few shot » Semantic parsing
