Amortizing intractable inference in large language models (2310.04363v2)

Abstract: Autoregressive LLMs compress knowledge from their training data through next-token conditional distributions. This limits tractable querying of this knowledge to start-to-end autoregressive sampling. However, many tasks of interest -- including sequence continuation, infilling, and other forms of constrained generation -- involve sampling from intractable posterior distributions. We address this limitation by using amortized Bayesian inference to sample from these intractable posteriors. Such amortization is algorithmically achieved by fine-tuning LLMs via diversity-seeking reinforcement learning algorithms: generative flow networks (GFlowNets). We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training and reward-maximizing policy optimization. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem and demonstrate that our approach enables data-efficient adaptation of LLMs to tasks that require multi-step rationalization and tool use.

Amortizing Intractable Inference in LLMs

The paper addresses the challenge of performing intractable inference in autoregressive LLMs by employing amortized Bayesian inference via fine-tuning with generative flow networks (GFlowNets). This work targets common tasks involving LLMs, such as sequence continuation, text infilling, and constrained text generation, which require sampling from complex posterior distributions that are typically computationally intractable.

Autoregressive LLMs encode their training knowledge by predicting the next token in a sequence given its preceding context, which is efficient for left-to-right generation. Nonetheless, this approach poses limitations for tasks that demand sampling conditioned on non-continuous segments of text or involve intricate constraints. To circumvent these limitations, the paper proposes a novel method that utilizes GFlowNets to fine-tune LLMs, making them capable of drawing samples from intractable distributions effectively.

Key Contributions

1. Algorithm for Amortized Sampling: A general algorithm is introduced for sampling from intractable LLM posterior distributions using GFlowNets. This approach provides an alternative to existing methods such as maximum likelihood training and reward-maximization strategies in reinforcement learning (RL).
2. Probabilistic Approach to Fine-Tuning: The method interprets chain-of-thought reasoning as a latent variable modeling challenge. This interpretation aids in modeling complex inference as a Bayesian problem, thereby providing a probabilistic basis for fine-tuning LLMs for tasks requiring sequential rationalization and decision-making.
3. Empirical Validation: The efficacy of GFlowNet fine-tuning is demonstrated across multiple applications, including sentence continuation, story infilling, subjectivity classification, and tool-assisted arithmetic problem-solving. The approach improves not only the diversity and quality of generated samples but also offers significant enhancements in generalization and data efficiency.

Implications

Practical

The development of GFlowNet fine-tuning enhances the practical application of LLMs, making them more versatile in handling tasks that involve generating text with complex constraints or reasoning steps. By enabling more accurate and diverse sampling from the model's learned distribution, this approach has the potential to improve text generation tasks in various applications including creative writing, automatic code generation, and AI-driven decision support systems.

Theoretical

The integration of a probabilistic inference framework with LLMs could reshape how we understand neural text generation. The research builds upon the Bayesian framework by demonstrating that amortized inference can offer more robust solutions compared to classic RL approaches that often struggle with reward misspecification and mode collapse. The insights gained from this work may influence future developments in designing and fine-tuning LLMs, suggesting a shift towards distribution-matching objectives over traditional reward-driven methods.

Future Directions

Further exploration could extend the application of this methodology to larger models and diverse types of tasks. The paper indicates opportunities in leveraging GFlowNets for creating more generalized reasoning models that could potentially handle a broader spectrum of complex inferential tasks. Additionally, investigating how this architecture might enable structured reasoning using tree or graph-based models could open new avenues in AI research.

The limitations stated, such as constraints on model size due to resource limitations and the focus on inference rather than knowledge representation, suggest a fruitful ground for continued research. Addressing these limitations would enhance the capability of LLMs to manage even more complex inferential needs while improving their practical utility.

In summary, this paper presents a viable pathway for enhancing the inference capabilities of LLMs, drawing connections between Bayesian probability, neural network training, and practical application in contemporary AI research. The use of GFlowNets signifies a promising shift towards more accurate and flexible text generation paradigms, with both immediate and long-term implications for the development of intelligent systems.