Summary of Concept Based Continuous Prompts For Interpretable Text Classification, by Qian Chen et al.
Concept Based Continuous Prompts for Interpretable Text Classification
by Qian Chen, Dongyang Li, Xiaofeng He
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a novel framework for interpreting continuous prompts in natural language processing. Building upon Concept Bottleneck Models, the authors decompose continuous prompts into human-readable concepts using a concept embedding matrix and coefficient matrix. The framework uses GPT-4o to generate a concept pool and selects potential candidate concepts through a submodular optimization algorithm. Experimental results show that this approach achieves similar performance to P-tuning and word-based methods while providing more interpretable results. The authors also provide code for their method at GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how continuous prompts work better in natural language tasks. Right now, scientists think about individual words when interpreting these prompts, but that’s not enough. The researchers behind this study wanted to find a way to break down the prompt into smaller ideas or concepts that humans can understand. They created a new method that uses a special kind of AI called GPT-4o to generate a list of possible concepts and then chooses the best ones using a clever algorithm. This approach helps us get better results in language tasks and also makes it easier for us to see why the AI is making certain predictions. |
Keywords
» Artificial intelligence » Embedding » Gpt » Natural language processing » Optimization » Prompt
