PTR: Prompt Tuning with Rules for Text Classification (2105.11259v3)

Abstract: Fine-tuned pre-trained LLMs (PLMs) have achieved awesome performance on almost all NLP tasks. By using additional prompts to fine-tune PLMs, we can further stimulate the rich knowledge distributed in PLMs to better serve downstream tasks. Prompt tuning has achieved promising results on some few-class classification tasks such as sentiment classification and natural language inference. However, manually designing lots of language prompts is cumbersome and fallible. For those auto-generated prompts, it is also expensive and time-consuming to verify their effectiveness in non-few-shot scenarios. Hence, it is still challenging for prompt tuning to address many-class classification tasks. To this end, we propose prompt tuning with rules (PTR) for many-class text classification and apply logic rules to construct prompts with several sub-prompts. In this way, PTR is able to encode prior knowledge of each class into prompt tuning. We conduct experiments on relation classification, a typical and complicated many-class classification task, and the results show that PTR can significantly and consistently outperform existing state-of-the-art baselines. This indicates that PTR is a promising approach to take advantage of both human prior knowledge and PLMs for those complicated classification tasks.

Analysis of "PTR: Prompt Tuning with Rules for Text Classification"

The paper by Han et al. introduces the method of Prompt Tuning with Rules (PTR) for enhancing text classification, specifically targeting many-class classification tasks. This approach builds upon the capabilities of pre-trained LLMs (PLMs) by combining prompt tuning with logic rules, offering an innovative strategy that seeks to address the existing limitations in manually designing effective prompts for many-class scenarios.

Core Contributions

The core idea of PTR lies in its strategic use of logic rules to construct prompts using sub-prompts. This method differentiates itself from traditional prompt tuning by:

1. Prior Knowledge Encoding: PTR leverages logic rules to integrate prior human knowledge about tasks directly into the prompt design process. For example, in relation classification, the method can encode relational semantics of sentences and entity types by combining various conditional functions.
2. Efficient Prompt Design: Instead of crafting templates and label words for each class, PTR simplifies this process by creating simple sub-prompts and then applying logic rules to form comprehensive task-specific prompts.

Methodological Approach

PTR involves the manual design of essential sub-prompts for each class and the use of logic rules to intelligently combine these sub-prompts. The paper provides a thorough exposition of how conditional functions are developed and how they contribute to the final task-specific prompts. In relation classifications, for example, these functions discern particular relational information, such as entity typing or relational semantics.

Experimental Results

The authors present a robust evaluation of PTR on relation classification tasks using datasets such as TACRED, TACREV, ReTACRED, and SemEval 2010 Task 8. The findings consistently indicate that PTR outperforms existing fine-tuning and prompt-based tuning methods:

• Significant Gains: Across different datasets, PTR achieves superior $F_1$ scores compared to state-of-the-art methods, demonstrating its ability to stimulate task-specific knowledge better than conventional techniques.
• Efficiency and Generalization: PTR does not require additional neural layers or human annotations, which evidences its computational efficiency and scalability to other text classification tasks.
• Few-shot Learning: PTR's efficacy in few-shot learning scenarios is particularly noteworthy, outperforming other prompt-based models without the need for extra data.

Comparative Analysis

The paper highlights how PTR surpasses both traditional fine-tuning and recent prompt-based techniques. The innovation of sub-prompt composition and logic rules offers an intuitive, yet structured, mechanism that bridges the gap between human prior knowledge and model understanding.

Implications and Future Directions

The implications of PTR are substantial in the field of NLP, especially for tasks involving large class sets where traditional methods struggle. By demonstrating how logic rules and sub-prompts can effectively harness PLM capabilities, PTR sets a precedent for integrating structured human knowledge with model-driven approaches.

Future research could explore the combination of PTR with automated prompt generation techniques to further optimize prompt design. The adaptability and effectiveness of PTR also warrant further investigation into more complex NLP applications beyond relation classification.

Overall, the paper presents a significant advancement in the domain of text classification, offering a novel approach to prompt tuning that balances efficiency, effectiveness, and human workload.