Summary of Leveraging Open Knowledge For Advancing Task Expertise in Large Language Models, by Yuncheng Yang et al.
Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models
by Yuncheng Yang, Yulei Qin, Tong Wu, Zihan Xu, Gang Li, Pengcheng Guo, Hang Shao, Yuchen Shi, Ke Li, Xing Sun, Jie Yang, Yun Gu
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 approach bridges the knowledge gap in large language models (LLMs) for domain-specific deployment by introducing few human-annotated samples (K-shot) to advance task expertise. An efficient pipeline is developed to produce task experts, selecting the most promising expert candidates and task-relevant instructions through a mixture-of-expert (MoE) system. The approach ensures models with problem-solving abilities are selected and prioritizes instructions sharing task-relevant contexts with K-shot. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need special tuning for specific tasks, which requires lots of effort and resources. A new method uses open knowledge to help train these models more efficiently. It’s like using a map to find the best route instead of exploring every path. This approach shows that by using a small amount of human-annotated data, we can make language models better at specific tasks. |
