Summary of Probabilistic Inference in Language Models Via Twisted Sequential Monte Carlo, by Stephen Zhao et al.
Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
by Stephen Zhao, Rob Brekelmans, Alireza Makhzani, Roger Grosse
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 framework leverages Sequential Monte Carlo methods for probabilistic inference in Large Language Models. The approach focuses inference-time computation on promising partial sequences by estimating the expected future value of a given reward or potential function at each timestep. This is achieved through learned twist functions, which are learned using a novel contrastive method. The framework also includes methods for evaluating the accuracy of language model inference techniques using bidirectional SMC bounds on the log partition function. Applications of the framework include sampling undesirable outputs from a pretrained model, generating reviews with varied sentiment, and performing infilling tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new approach uses computer simulations to improve the way large language models work. This is done by adjusting the “twist” or direction of the simulation based on what we want the model to do. The method also includes ways to check if the model is making accurate predictions, which can help prevent it from producing undesirable outputs. This technique can be used for tasks like generating reviews with different emotions and filling in missing information. |
Keywords
» Artificial intelligence » Inference » Language model
