COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics (2202.11705v3)

Abstract: Many applications of text generation require incorporating different constraints to control the semantics or style of generated text. These constraints can be hard (e.g., ensuring certain keywords are included in the output) and soft (e.g., contextualizing the output with the left- or right-hand context). In this paper, we present Energy-based Constrained Decoding with Langevin Dynamics (COLD), a decoding framework which unifies constrained generation as specifying constraints through an energy function, then performing efficient differentiable reasoning over the constraints through gradient-based sampling. COLD decoding is a flexible framework that can be applied directly to off-the-shelf left-to-right LLMs without the need for any task-specific fine-tuning, as demonstrated through three challenging text generation applications: lexically-constrained generation, abductive reasoning, and counterfactual reasoning. Our experiments on these constrained generation tasks point to the effectiveness of our approach, both in terms of automatic and human evaluation.

An Academic Perspective on Energy-based Constrained Text Generation with Langevin Dynamics

The paper "COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics" introduces a distinctive approach to text generation that integrates constraints within an energy-based model. This paper endeavors to address a common hurdle in automatic text generation: producing fluent text that adheres to various semantic and stylistic constraints. This challenge is pronounced in applications necessitating hard constraints, like inclusion of specific keywords, or soft constraints, such as maintaining coherence with contextual information.

Core Contributions and Methodology

The central thrust of the paper is the introduction of a decoding framework the authors refer to as “COLD” (Constrained Decoding with Langevin Dynamics). This framework capitalizes on the flexibility of energy-based models (EBMs) by formulating constrained text generation as an optimization problem. The constraints are encapsulated in an energy function, with generation samples drawn via Langevin dynamics, a method that bridges continuous approximations and discrete text sampling.

COLD decoding distinguishes itself by leveraging off-the-shelf left-to-right LLMs without necessitating task-specific fine-tuning. The framework is applicable to diverse generation tasks, evidenced by experiments on lexically constrained generation, abductive reasoning, and counterfactual reasoning.

Moreover, the paper creatively employs Langevin dynamics, typically reserved for continuous spaces, by first extending text sequences into a 'soft' continuous space, facilitating gradient-based optimization. This methodological innovation mitigates the challenge of sampling from discrete text EBMs, setting a precedent for future decoding strategies.

Empirical Evaluations

The empirical evaluation of COLD decoding is conducted rigorously across three challenging generation tasks:

1. Lexically Constrained Generation: The method excels in ensuring high keyword coverage compared to established techniques like NeuroLogic and TSMH, albeit with a trade-off in language fluency when measured by perplexity.
2. Abductive Reasoning: COLD decoding attains superior coherence—both overall and particularly with right-hand context—compared to DeLorean, the latter being notable for such tasks. It showcases a balanced performance, maintaining grammaticality while achieving enhanced coherence with narrative constraints.
3. Counterfactual Story Generation: In this task, the method outshines baselines by achieving high scores in both maintaining minimal edits from an original story ending and ensuring coherence with a counterfactual context.

The paper underlines that COLD decoding’s sampling approach allows for multi-sample generation and selection based on tailored criteria, improving flexibility over deterministic counterparts.

Theoretical and Practical Implications

The theoretical implications of this work lie in its demonstration of convergence properties of Langevin dynamics-based sampling in generating linguistic data, opening research avenues into further exploration of continuous-to-discrete domain transitions in text processing.

Practically, the successful application of COLD decoding to various constrained text generation tasks without task-specific model retraining underscores its potential as a versatile tool. For practitioners and AI systems developers, this suggests a reliable path forward in tasks requiring complex constraint management without the overhead of extensive data annotation or model customization.

Speculation on Future Developments

Looking ahead, the introduction of COLD decoding may inspire similar methods that incorporate differentiable reasoning across broader classes of discrete data or that integrate with training infrastructures for EBMs in supervised settings. Furthermore, this framework’s adaptability suggests potential extensions in dialogue systems where dynamic constraint adaptation is crucial.

The paper, while offering comprehensive insights into the proposed methodology and validations, leaves open questions about scalability and integration with other modalities, posing fertile ground for subsequent exploration.

In summation, "COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics" advances the field of constrained text generation via a novel, flexible, and effective approach, and stands as a valuable reference point for future developments in natural language processing.