Summary of Enhancing Few-shot Transfer Learning with Optimized Multi-task Prompt Tuning Through Modular Prompt Composition, by Ahmad Pouramini et al.
Enhancing Few-Shot Transfer Learning with Optimized Multi-Task Prompt Tuning through Modular Prompt Composition
by Ahmad Pouramini, Hesham Faili
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 A novel approach to multi-task prompt tuning enhances transfer learning and improves task performance by decomposing prompts into shared and private components, fine-tuning source prompts, and integrating them with private prompts. The proposed methods are evaluated on the GLUE benchmark and other tasks, demonstrating superior few-shot performance and robustness compared to conventional prompt tuning and related works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes a step forward in multi-task learning by exploring how prompts can be shared between different tasks. By breaking down prompts into parts that are common across tasks (source prompts) and unique to each task (private prompts), the authors show how fine-tuning these source prompts can help improve performance on new, unseen tasks. The results are impressive, with significant improvements in accuracy and robustness compared to previous methods. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Multi task » Prompt » Transfer learning
