Summary of A Learn-then-reason Model Towards Generalization in Knowledge Base Question Answering, by Lingxi Zhang and Jing Zhang and Yanling Wang and Cuiping Li and Hong Chen
A Learn-Then-Reason Model Towards Generalization in Knowledge Base Question Answering
by Lingxi Zhang, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen
First submitted to arxiv on: 20 Jun 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 new framework for Knowledge Base Question Answering (KBQA) called KBLLaMA, which learns from large-scale knowledge bases like Freebase and Wikidata. The retrieve-then-reason approach used in previous studies prioritizes external sources but neglects the incorporation of new knowledge into model parameters, limiting generalization capabilities. To address this, KBLLaMA uses a learn-then-reason framework to inject new KB knowledge into a large language model for flexible end-to-end KBQA. The paper investigates how to organize and facilitate learning from new KB knowledge, achieving state-of-the-art performance on various KBQA tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KBLLaMA is a new way to ask questions using big databases like Freebase and Wikidata. These databases have lots of useful information, but previous methods for asking questions didn’t use all the data. The new approach learns from the database itself instead of just relying on external sources. This helps KBQA models be more accurate and flexible when answering questions. |
Keywords
» Artificial intelligence » Generalization » Knowledge base » Large language model » Question answering
