Summary of Exploring Embedding Priors in Prompt-tuning For Improved Interpretability and Control, by Sergey Sedov et al.
Exploring Embedding Priors in Prompt-Tuning for Improved Interpretability and Control
by Sergey Sedov, Sumanth Bharadwaj Hachalli Karanam, Venu Gopal Kadamba
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 paper investigates the phenomenon of embedding collapse in Prompt-Tuning, an efficient method for adapting pre-trained language models to new tasks with minimal computational overhead. The authors design embedding priors and compare them with posteriors of the converged Soft and Deep Prompt-Tuning methods, finding that priors strongly affect the position of tuned embeddings. The study suggests that controllable Prompt-Tuning posteriors may serve as a good starting point for tasks like chain-of-thought distillation. Experiments also show distinct clusters of activations for different tasks, raising questions about the importance of activation clusters for generalization abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make language models better at new tasks without using too much computer power. It’s about a way called Prompt-Tuning that helps models learn faster and more accurately. The researchers found out that the way they started with the model matters, and that some starting points work better than others. They also discovered that different types of tasks create different patterns in how the model works, which could help us make even better models. |
Keywords
» Artificial intelligence » Distillation » Embedding » Generalization » Prompt
