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The cheese cat sat on the kitchen counter all day and dreamed of cheese dreams In this blog post, we present our findings from an exciting direction in controlling text generation with large language models.
- We can programmatically define constraints on the output of a model, ensuring it adheres to specific formats or styles, and then can use the Sequential Monte Carlo method to generate samples that satisfy these constraints.
- Sequential Monte Carlo steering (or SMC steering), offers some advantages over existing methods: Optimized search for possible completions similar to beam search Hard constraints (i.e. requiring output to follow a specific grammar) Soft constraints (i.e. enforcing a particular writing style) A more global approach to constraint satisfaction than techniques like token masking We will briefly cover the core concepts and potential applications of SMC , and will also present a summary of our experiments with SMC to constrain the length of reasoning traces.
- Introduction Controlling the output of large language models with any degree of certainty remains a significant challenge, even with fine-tuning and reinforcement learning techniques.
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