Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning (2305.13971v6)

Abstract: Despite their impressive performance, LLMs (LMs) still struggle with reliably generating complex output structures when not finetuned to follow the required output format exactly. To address this issue, grammar-constrained decoding (GCD) can be used to control the generation of LMs, guaranteeing that the output follows a given structure. Most existing GCD methods are, however, limited to specific tasks, such as parsing or code generation. In this work, we demonstrate that formal grammars can describe the output space for a much wider range of tasks and argue that GCD can serve as a unified framework for structured NLP tasks in general. For increased flexibility, we introduce input-dependent grammars, which allow the grammar to depend on the input and thus enable the generation of different output structures for different inputs. We then empirically demonstrate the power and flexibility of GCD-enhanced LMs on (1) information extraction, (2) entity disambiguation, and (3) constituency parsing. Our results indicate that grammar-constrained LMs substantially outperform unconstrained LMs or even beat task-specific finetuned models. Grammar constraints thus hold great promise for harnessing off-the-shelf LMs for a wide range of structured NLP tasks, especially where training data is scarce or finetuning is expensive. Code and data: https://github.com/epfl-dlab/GCD.

• The paper presents a framework that enforces strict output structures using formal grammars without additional finetuning.
• It introduces input-dependent grammars that adapt to specific inputs, rivaling or surpassing finetuned models in tasks like entity disambiguation.
• Empirical results confirm that Grammar-Constrained Decoding offers a cost-effective and robust alternative for structured NLP tasks with limited training data.

Grammar-Constrained Decoding for Structured NLP Tasks Without Finetuning

This paper presents a comprehensive paper on Grammar-Constrained Decoding (GCD) as a means to improve the performance of LLMs in structured NLP tasks, without requiring additional finetuning. The research addresses the shortcomings of LLMs in scenarios where output must strictly adhere to a predefined structure, citing tasks such as information extraction, entity disambiguation, and constituency parsing.

Core Contributions

1. Unified Framework through Formal Grammars: The authors argue for viewing structured NLP tasks through the lens of formal grammars, presenting a unified framework that uses grammar-constrained decoding to enforce structured outputs during the inference stage. The paper distinguishes itself by extending the applicability of formal grammars beyond task-specific applications (like parsing or entity recognition) to a broader array of NLP tasks.
2. Input-Dependent Grammars: To enhance flexibility and applicability, the notion of input-dependent grammars is introduced. This novel approach allows the grammar to adapt based on input, facilitating more precise output structures tailored to the particular input instance.
3. Empirical Demonstration: The paper includes robust experiments to verify the effectiveness of the GCD framework. In tasks like closed information extraction, entity disambiguation, and constituency parsing, the method reportedly rivals or surpasses existing finetuned models, emphasizing the utility of GCD when training data is limited.

Detailed Insights

• Technical Framework: The research outlines a rigorous methodology where grammar constraints are superimposed on LLM outputs using formal grammars approximating context-free grammars. The incremental parsing serves to guide the LLM in adhering to these constraints, thereby ensuring that generated outputs are not just coherent but also valid according to the task's structural requirements.
• Performance Metrics: Numerical results underscore the strengths of the approach. For example, the LLaMA-33B model constrained with GCD achieved a significant improvement over unconstrained models, even outperforming dedicated task-specific finetuned models in some cases.
• Implications and Future Directions: This work offers substantial implications for the practical deployment of NLP systems, particularly where finetuning is impractical due to cost or data scarcity. The paper suggests that GCD could serve as an efficient intermediary step, allowing practitioners to harness pretrained LLMs effectively without further training. It also highlights the promise of more universal applicability of LLMs to structured prediction tasks.

Practical and Theoretical Impact

The approach outlined in this paper challenges traditional NLP methodologies by proposing an alternative path to adapting LLMs for structured tasks. The use of GCD is presented as a cost-effective, adaptable method that can be rapidly implemented across a wide spectrum of tasks. In a theoretical context, it postulates a broad-reaching framework that can influence future NLP model designs and methodologies.

The research speculatively heralds advancements in NLP where traditional finetuning is seen as less critical, with GCD providing a robust pathway, thus potentially transforming the landscape of LLM utility and applicability.

Conclusion

In sum, this paper provides a compelling argument and empirical evidence for Grammar-Constrained Decoding as a potent mechanism to enhance the capability of LLMs in structured NLP tasks. It invites future explorations into more complex tasks, improved parsers for enhanced latency, and further integration with emerging models, underscoring its significant potential to shift prevailing strategies in AI-driven text processing.