Summary of On Few-shot Prompting For Controllable Question-answer Generation in Narrative Comprehension, by Bernardo Leite and Henrique Lopes Cardoso
On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative Comprehension
by Bernardo Leite, Henrique Lopes Cardoso
First submitted to arxiv on: 3 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 The paper presents a few-shot prompting strategy for controlling the generation of question-answer pairs from children’s narrative texts. The goal is to control two attributes: the explicitness of questions and the underlying narrative elements. A reference model is used as a baseline, and the proposed approach is evaluated using empirical experiments. Results show that the few-shot strategy outperforms the reference model in some scenarios, such as semantic closeness evaluation and diversity/coherency of question-answer pairs. However, improvements are not always statistically significant. The study demonstrates the effectiveness of controlling the generation process using few-shot prompting, with implications for applications like education and text-based interfaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to generate questions based on stories written by children. The goal is to make sure the questions are clear and related to what’s being told in the story. The researchers tested this method against another approach and found that it works better in some cases, like making sure the questions are close to what’s being said in the story. While it doesn’t always work better, it shows promise for uses like helping kids learn or creating text-based games. |
Keywords
» Artificial intelligence » Few shot » Prompting
