Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs (2306.03081v2)

Abstract: Even after fine-tuning and reinforcement learning, LLMs can be difficult, if not impossible, to control reliably with prompts alone. We propose a new inference-time approach to enforcing syntactic and semantic constraints on the outputs of LLMs, called sequential Monte Carlo (SMC) steering. The key idea is to specify language generation tasks as posterior inference problems in a class of discrete probabilistic sequence models, and replace standard decoding with sequential Monte Carlo inference. For a computational cost similar to that of beam search, SMC can steer LLMs to solve diverse tasks, including infilling, generation under syntactic constraints, and prompt intersection. To facilitate experimentation with SMC steering, we present a probabilistic programming library, LLaMPPL (https://github.com/probcomp/hfppl), for concisely specifying new generation tasks as LLM probabilistic programs, and automating steering of LLaMA-family Transformers.

• The paper introduces Feynman-Kac Transformer models to effectively encode and enforce complex generation constraints through sequential Monte Carlo steering.
• It details an SMC algorithm with without-replacement resampling and a shared Transformer cache, optimizing computational efficiency similar to beam search.
• The authors provide LLaMPPL, a probabilistic programming library integrating LLaMA models, enabling users to specify and automate diverse constrained generation tasks.

Sequential Monte Carlo Steering of LLMs using Probabilistic Programs

This paper proposes a novel inference-time technique to control the outputs of LLMs through the use of sequential Monte Carlo (SMC) steering. The technique addresses limitations associated with fine-tuning and reinforcement learning methods, which often fall short in reliably enforcing syntactic and semantic constraints via available prompting strategies. The method is particularly prominent for its application to a variety of constrained generation tasks under a framework that views such tasks as probabilistic inference problems.

Key Contributions

1. Feynman-Kac Transformer Models: The paper introduces a class of probabilistic models designed for application with SMC, named Feynman-Kac Transformer models. These models define a framework for representing complex language generation constraints, thereby enabling diverse tasks such as infilling and constrained generation.
2. SMC Transformer Steering: A tailored version of SMC is presented for use with Feynman-Kac Transformer models. The algorithm is noted for its computational cost efficiency, comparable to beam search, and incorporates advancements to handle particle degeneracy via a without-replacement resampling scheme and shared Transformer cache to optimize computational resources.
3. LLaMPPL Library: The authors present a probabilistic programming library, LLaMPPL, which integrates the LLaMA family of Transformers. The library allows users to specify new generation tasks as probabilistic programs, automating the SMC steering process.

Methodology

The paper details a comprehensive methodology for constrained generation framed as a probabilistic inference problem. It contrasts this with heuristic and optimization approaches often used in the literature, highlighting the fallback of greedy and local strategies that can fail to optimize for global constraints. The approach advocated uses globally informed probability distributions over token sequences, characterized by SMC’s ability to reallocate probability mass intelligently across tokens.

Several examples are provided to illustrate the application of Feynman-Kac models, from simple length constraints to more complex tasks like template infilling and prompt intersection. This flexibility in composition and conditioning enables users to articulate nuanced generation constraints efficiently within the probabilistic programming paradigm.

Implications and Future Directions

The implications of this research are substantial for artificial intelligence, particularly in developing more controlled, reliable generative LLMs. By reframing language generation tasks as posterior inference problems, the approach offers a robust alternative that circumvents the pitfalls of local decoding biases and greedy heuristics.

In terms of practical applications, SMC steering provides computational tractability for complex generation tasks while maintaining diversity in output—an endeavor that beam search, despite its optimizational strengths, struggles with due to inherent length biases.

Future work might explore integrating richer proposal distributions and extending support to other Transformer-backed models beyond the LLaMA family. Additionally, advances in probabilistic programming can leverage these findings to automate and optimize proposals for enhanced steering efficacy, providing an avenue for more accurate posterior samples in increasingly complex models.

Overall, the work contributes to the ongoing development of probabilistic programming frameworks that seamlessly incorporate LLMs, thereby enhancing model utility across a broad spectrum of natural language processing tasks.