Summary of Contrastive Learning For Knowledge-based Question Generation in Large Language Models, by Zhenhong Zhang et al.
Contrastive Learning for Knowledge-Based Question Generation in Large Language Models
by Zhenhong Zhang, Jiajing Chen, Weiyan Shi, Lingjie Yi, Chihang Wang, Qian Yu
First submitted to arxiv on: 21 Sep 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 proposed knowledge-based question generation technology aims to enable computers to simulate human questioning processes based on understanding specific texts or knowledge bases. To address issues of hallucination and knowledge gaps in large-scale language models, an enhanced method incorporates contrastive learning. This approach utilizes multiple models to jointly mine domain knowledge and guides the model in reducing noise and hallucinations through contrasting examples. Experimental results show that using prompts with contrasting examples improves performance, particularly when combining contrasting instructions and examples, leading to higher-quality generated questions and improved accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence technology is getting smarter! This paper talks about how computers can ask better questions, like humans do. It’s all about understanding specific texts or knowledge bases. Right now, big language models have some problems with this task because they sometimes make things up or don’t know the answers. To fix this, the researchers propose a new method that uses multiple models to work together and learn from each other. This helps reduce mistakes and makes the computer-generated questions better. The results show that this approach is really effective in improving question quality and accuracy. |
Keywords
» Artificial intelligence » Hallucination
