Summary of Graph Guided Question Answer Generation For Procedural Question-answering, by Hai X. Pham et al.
Graph Guided Question Answer Generation for Procedural Question-Answering
by Hai X. Pham, Isma Hadji, Xinnuo Xu, Ziedune Degutyte, Jay Rainey, Evangelos Kazakos, Afsaneh Fazly, Georgios Tzimiropoulos, Brais Martinez
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a novel approach to generating task-specific question answering (QA) models that are compact and competitive with GPT variants. The key innovation is a mechanism for automatically generating training data from procedural text, which allows for the creation of exhaustive and high-quality training sets. This method leverages graph-based representations to condition on step-by-step instructions and produce QA pairs in an exhaustive and controllable manner. The authors demonstrate that small models trained with their generated data can achieve excellent performance on target QA tasks, even surpassing larger language models like GPT3 and ChatGPT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make question-answering machines (QA) smaller and better at answering specific questions. They created a special method that uses text instructions to make lots of training data for these QA machines. This helps them learn quickly and accurately answer questions without needing to be super big or powerful like some other language models. The results show that even small QA machines can do well if they’re trained with the right data. |
Keywords
» Artificial intelligence » Gpt » Question answering
