Summary of Sgsh: Stimulate Large Language Models with Skeleton Heuristics For Knowledge Base Question Generation, by Shasha Guo et al.
SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation
by Shasha Guo, Lizi Liao, Jing Zhang, Yanling Wang, Cuiping Li, Hong Chen
First submitted to arxiv on: 2 Apr 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 This paper explores how to effectively leverage large language models (LLMs) like GPT-3.5 for knowledge base question generation (KBQG). Building upon existing methods that utilize pre-trained language models, the authors propose a simple and effective framework called SGSH that stimulates LLMs with skeleton heuristics to generate optimal questions. The framework incorporates “skeleton heuristics” that provide fine-grained guidance for each input, encompassing essential elements like question phrases and auxiliary verbs. To construct a training dataset, the authors use ChatGPT to create skeletons based on which they train a BART model to generate skeleton prompts. These prompts are then used to encode skeleton heuristics into GPT-3.5 prompts, incentivizing it to generate desired questions. Experimental results show that SGSH achieves state-of-the-art performance in KBQG tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to get computers to ask good questions based on information from a special kind of database called a knowledge base. Right now, computers are pretty good at answering questions, but they’re not very good at asking them. The authors want to change that by using something called large language models (LLMs) like GPT-3.5. They created a new way to use these LLMs called SGSH, which helps the computer ask better questions. To do this, they used another computer program called ChatGPT to create examples of good questions and then trained a special kind of model to make those examples into prompts that the LLM can understand. This makes it easier for the LLM to generate good questions. The results show that SGSH is really good at generating questions from knowledge bases. |
Keywords
» Artificial intelligence » Gpt » Knowledge base
