Summary of Accept: Adaptive Codebook For Composite and Efficient Prompt Tuning, by Yu-chen Lin et al.
ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning
by Yu-Chen Lin, Wei-Hua Li, Jun-Cheng Chen, Chu-Song Chen
First submitted to arxiv on: 10 Oct 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 Prompt Tuning has been a successful method for fine-tuning large-scale Language Models (PLMs) with minimal parameter updates. However, traditional Prompt Tuning methods consider each prompt independently, leading to an increase in parameters proportional to prompt length. To address this issue, we propose ACCEPT, a novel approach that leverages product quantization (PQ) to share learnable codebook vectors across prompts while adapting weights for each prompt. Our method achieves superior performance on 17 diverse natural language tasks, including NLU and QA tasks, by tuning only 0.3% of the PLM’s parameters. ACCEPT also excels in few-shot and large model settings, demonstrating its significant potential. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models work better with less information. Right now, when we give these models a prompt to follow, they can get confused if the prompt is long or complex. To fix this, the authors came up with a new way of fine-tuning these models called ACCEPT. It’s like having a special set of building blocks that all prompts use, but each prompt also has its own unique instructions. This makes the models work better and faster, even when they’re big and complicated. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Prompt » Quantization
