Summary of Towards Probabilistically-sound Beam Search with Masked Language Models, by Creston Brooks et al.
Towards Probabilistically-Sound Beam Search with Masked Language Models
by Creston Brooks, Robert Calef, Charlie Cowen-Breen, Anna Sappington
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed methods offer a probabilistically-sound approach to beam search with masked language models (MLMs), which is crucial for domain-specific applications like ancient text restoration and protein engineering. By clarifying the conditions under which standard beam search is theoretically sound, and providing an inference time modification that maintains no additional computational complexity, the authors improve upon existing methods. Empirical results demonstrate the superiority of this approach across various domains, highlighting the importance of probing MLMs’ inductive biases and exploring their contextual sensitivity for text infilling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Beam search with masked language models is tricky because we can’t easily find the right probabilities for sequences. But figuring out these probabilities is important for things like restoring old texts and engineering new proteins. To solve this problem, the researchers present two new methods that work well together. The first method helps us understand when we can use a standard beam search approach, and the second method makes adjustments to improve results when those conditions aren’t met. They tested these methods on different tasks and showed they’re better than what’s currently available. |
Keywords
* Artificial intelligence * Inference
