Promptly Predicting Structures: The Return of Inference (2401.06877v3)

Abstract: Prompt-based methods have been used extensively across NLP to build zero- and few-shot label predictors. Many NLP tasks are naturally structured: that is, their outputs consist of multiple labels which constrain each other. Annotating data for such tasks can be cumbersome. Can the promise of the prompt-based paradigm be extended to such structured outputs? In this paper, we present a framework for constructing zero- and few-shot linguistic structure predictors. Our key insight is that we can use structural constraints -- and combinatorial inference derived from them -- to filter out inconsistent structures predicted by LLMs. We instantiated this framework on two structured prediction tasks, and five datasets. Across all cases, our results show that enforcing consistency not only constructs structurally valid outputs, but also improves performance over the unconstrained variants.

• The paper presents an innovative inference framework that integrates with prompt-based methods to ensure structurally valid predictions.
• It demonstrates significant performance enhancements across tasks like Semantic Role Labeling and coreference resolution, reducing inconsistencies.
• The approach leverages LLMs without task-specific training, offering a cost-effective and scalable solution for complex NLP structured prediction challenges.

Introduction to Structured Prediction

Structured prediction is a critical area in NLP aimed at decoding complex relations within data, such as parsing grammatical structures or understanding semantic roles in a sentence. The task requires models to make multiple, interrelated decisions, posing a challenge for accurate and efficient prediction models.

Prompt-Based Paradigm and LLMs

With the advent of LLMs, the prompt-based paradigm has gained popularity. In this method, an LLM generates output based on a textual prompt. The advantage of this approach lies in its versatility, as it can extend to a multitude of tasks using the same pre-trained models without the need for extensive labeled datasets. For zero-shot or few-shot applications—where a model sees few or no examples before it's put to use—prompt-based methods have shown promising results.

However, for structured prediction, the application of such methods has been underexplored. While previous attempts have utilized prompts to resolve individual components without considering their interdependence, leading to structurally invalid or inconsistent outputs.

The Return of Inference

In a recent development, researchers have presented a framework that reintroduces inference to the prompt-based approach for structured predictions. The framework filters out inconsistent predictions by leveraging the constraints inherent to the structure of the target output. This innovation uses sophisticated algorithms that work with LLMs to ensure the validity of the output structure.

In experiments across various structured prediction tasks, the application of this framework has not only enhanced the quality of generated data by enforcing consistency but also improved overall performance compared to unconstrained versions. Importantly, this approach does not require task-specific training, as it utilizes LLMs in conjunction with inference algorithms.

Exemplary Results and Implications

The practical implications of this are profound. For instance, in Semantic Role Labeling—a task that determines the relationships and roles in a sentence—the framework provided considerable improvements in both consistency and performance metrics. Furthermore, for the intricate task of coreference resolution, which groups mentions of the same entity within a text, significant gains in accuracy were achieved.

This progress suggests that incorporating inference can push the boundaries of what is achievable with prompt-based, few-shot methods in structured prediction tasks. The research indicates that structurally valid outputs are possible even with limited exposure to task-specific data, presenting a cost-effective and time-efficient direction for future NLP endeavors.