Summary of Qog:question and Options Generation Based on Language Model, by Jincheng Zhou
QOG:Question and Options Generation based on Language Model
by Jincheng Zhou
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 novel approach to Question-Options Generation (QOG), which involves generating question-options pairs given context. This task has significant applications, including fine-tuning large models for information retrieval and automated multiple-choice question generation for education. The authors develop QOG models using three different methods based on fine-tuning sequence-to-sequence language models, demonstrating that the end-to-end approach is computationally efficient and stable during training and inference. Notably, this method outperforms other approaches, with QOG models showing competitiveness compared to the large language model Llama 3-8B. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new task called Question-Options Generation (QOG), which helps generate question-options pairs based on context. This can be useful in many areas, such as making big AI models smarter and helping people learn more effectively. The researchers came up with three different ways to do this using special language models that can translate sequences of words into other sequences. They found that one approach was particularly good at generating question-options pairs efficiently and accurately. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Large language model » Llama
