Summary of What Are the Essential Factors in Crafting Effective Long Context Multi-hop Instruction Datasets? Insights and Best Practices, by Zhi Chen et al.
What are the Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best Practices
by Zhi Chen, Qiguang Chen, Libo Qin, Qipeng Guo, Haijun Lv, Yicheng Zou, Wanxiang Che, Hang Yan, Kai Chen, Dahua Lin
First submitted to arxiv on: 3 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 Recent advancements in large language models (LLMs) have improved tasks such as information extraction, question answering, and complex planning scenarios. To enhance the long context capabilities of LLMs, synthetic data has been generated through existing methods like Self-Instruct. However, preliminary experiments show that less than 35% of generated samples are multi-hop, with over 40% exhibiting poor quality, limiting comprehensive understanding and further research. To improve data quality, we propose the Multi-agent Interactive Multi-hop Generation (MIMG) framework, which incorporates a Quality Verification Agent, Single-hop Question Generation Agent, Multiple Question Sampling Strategy, and Multi-hop Question Merger Agent. This framework improves data quality, with high-quality, multi-hop, and diverse data exceeding 85%. We also investigate document selection, question merging, and validation techniques through extensive experiments across various models. Our findings show that synthetic high-quality long-context instruction data significantly enhances model performance, even surpassing models trained on larger amounts of human-annotated data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have been working to improve large language models (LLMs) so they can understand longer pieces of text. They’ve been using fake data to help them learn, but this data hasn’t been very good. Most of it is just short questions or answers and doesn’t really help the model learn. To fix this, a new way of generating fake data has been developed. This method uses multiple agents working together to create better questions and answers that are more helpful for learning. The results show that this new method produces much higher-quality data that helps the models perform better. |
Keywords
» Artificial intelligence » Question answering » Synthetic data
