Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo (2404.17546v1)

Abstract: Numerous capability and safety techniques of LLMs, including RLHF, automated red-teaming, prompt engineering, and infilling, can be cast as sampling from an unnormalized target distribution defined by a given reward or potential function over the full sequence. In this work, we leverage the rich toolkit of Sequential Monte Carlo (SMC) for these probabilistic inference problems. In particular, we use learned twist functions to estimate the expected future value of the potential at each timestep, which enables us to focus inference-time computation on promising partial sequences. We propose a novel contrastive method for learning the twist functions, and establish connections with the rich literature of soft reinforcement learning. As a complementary application of our twisted SMC framework, we present methods for evaluating the accuracy of LLM inference techniques using novel bidirectional SMC bounds on the log partition function. These bounds can be used to estimate the KL divergence between the inference and target distributions in both directions. We apply our inference evaluation techniques to show that twisted SMC is effective for sampling undesirable outputs from a pretrained model (a useful component of harmlessness training and automated red-teaming), generating reviews with varied sentiment, and performing infilling tasks.

• The paper introduces a novel inference mechanism using learned twist functions within Sequential Monte Carlo to improve sampling efficiency and accuracy.
• It leverages Contrastive Twist Learning and reinforcement learning techniques to identify high-potential sequence continuations early in text generation.
• The evaluation framework employs log partition function estimation and symmetrized KL divergence to rigorously assess the quality of the sampling process.

Exploring Advanced Sampling in LLMs with Twisted Sequential Monte Carlo

Introduction to Twisted SMC

Ever grappled with converting raw potentials of LLMs into practical inference solutions? Twisted Sequential Monte Carlo (SMC) throws an interesting spin into this arena. Built on the back of traditional SMC techniques, it integrates learned twist functions to optimize sampling efficiency by zeroing in on promising partial sequences promising greater relevance and accuracy.

How Twisted SMC Works

Twisted SMC refines how models sample possible outputs, focusing computational efforts on high-potential sequences. Here's a breakdown of how it operates:

• Standard SMC: This methodology involves breaking down the sampling process into manageable parts, each of which better approximates the target distribution step by step.
• Adding a Twist: In twisted SMC, each step includes an adaptive function, or ‘twist,’ learned from data. This function predicts the quality of continuing a sequence, allowing the model to focus on the most promising sequences early in the generation process.

Learning Efficient Twists

How do we teach a model to identify these promising sequences efficiently? There are several methods explored in the paper:

• Contrastive Twist Learning (CTL): This new method teaches the model to distinguish between more and less promising sequences by comparing generated sequences against sequences sampled directly from the target distribution.
• Reinforcement Learning and Extensions: Drawing parallels to reinforcement learning, where an agent learns to make decisions by receiving rewards, the paper interprets these twist functions as a way of evaluating actions (sequence continuations) based on their expected future rewards.

Practical Implications for AI Development

The implications of such an efficient sampling mechanism are vast, particularly in tasks involving LLMs:

• Efficiency: By focusing computational resources on sequences that are more likely to be relevant, twisted SMC can greatly reduce the time and computational cost of generating text.
• Versatility: It can be adapted to a variety of tasks from generating creative text to optimizing responses in a dialogue system, always seeking to steer outputs to match desired criteria.

Evaluating Inference Techniques

The paper doesn't just develop a new way to sample sequences—it also provides a robust framework for evaluating how well different methods work, using:

• Log Partition Function Estimation: By bounding the normalization constant in probabilities, it offers a detailed view of how well the sampling distribution matches the target distribution.
• Symmetrized KL Divergence: This metric offers a two-way look at how the sampling and target distributions deviate from each other, providing a comprehensive measure of inference quality.

Future Directions

Twisted SMC offers a promising avenue for both research and practical applications. Future research could refine the learning of twist functions, extend the approach to other types of models beyond language, or explore new ways to quantify and minimize errors in complex sampling tasks.

Final Thoughts

By marrying the robustness of sequential Monte Carlo methods with the precision of machine-learned twist functions, twisted SMC represents a significant step forward in the efficiency and effectiveness of probabilistic inference in LLMs, imbuing them with a keen focus on sequence quality that's bound to push the boundaries of what's possible in AI-generated text.