Summary of A Voter-based Stochastic Rejection-method Framework For Asymptotically Safe Language Model Outputs, by Jake R. Watts et al.
A Voter-Based Stochastic Rejection-Method Framework for Asymptotically Safe Language Model Outputs
by Jake R. Watts, Joel Sokol
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 proposed method addresses the issue of low-quality outputs from large language models (LLMs) by introducing a voting system among LLM checkers. The approach regenerates outputs until a threshold of approval is reached, ensuring acceptable results. Estimators for cost and failure rate are developed, allowing an algorithm to achieve a target failure rate at the lowest possible cost. Experimental data demonstrates that the proposed models result in exponential decreases in failure rate with increasing cost when optimized parameters are used. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper suggests a way to prevent bad or low-quality outputs from large language models by having other models vote on whether they’re good or not. If most of them don’t think it’s good, they regenerate the output until most agree it’s acceptable. The paper also proposes ways to estimate how much this will cost and how often it will fail. They show that with their approach, failure rate decreases quickly as you spend more to get better results. |
