Summary of Controllable Generation Via Locally Constrained Resampling, by Kareem Ahmed et al.
Controllable Generation via Locally Constrained Resampling
by Kareem Ahmed, Kai-Wei Chang, Guy Van den Broeck
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 probabilistic approach utilizes Bayesian conditioning to generate samples subject to logical constraints in natural language modeling, addressing the limitations of current greedy methods. The approach considers the entire sequence, inducing a local, factorized distribution that can be tractably conditioned on the constraint. This allows for generating samples that satisfy the constraints and closely approximate the target distribution. The method is evaluated on tasks such as LLM detoxification and solving Sudoku puzzles, demonstrating its effectiveness in steering outputs away from toxic generations and achieving perfect accuracy on Sudoku. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to control what language models say using rules or constraints. Currently, these models can generate anything, even if it’s not true or respectful. The authors want to change this by making the model follow certain guidelines. They create a special distribution that takes into account all the words in a sentence and then use it to generate new sentences that respect the rules. This approach works well for tasks like removing toxic language from text and solving Sudoku puzzles. |
