Contrastive Demonstration Tuning for Pre-trained Language Models (2204.04392v4)

Abstract: Pretrained LLMs can be effectively stimulated by textual prompts or demonstrations, especially in low-data scenarios. Recent works have focused on automatically searching discrete or continuous prompts or optimized verbalizers, yet studies for the demonstration are still limited. Concretely, the demonstration examples are crucial for an excellent final performance of prompt-tuning. In this paper, we propose a novel pluggable, extensible, and efficient approach named contrastive demonstration tuning, which is free of demonstration sampling. Furthermore, the proposed approach can be: (i) Plugged into any previous prompt-tuning approaches; (ii) Extended to widespread classification tasks with a large number of categories. Experimental results on 16 datasets illustrate that our method integrated with previous approaches LM-BFF and P-tuning can yield better performance. Code is available in https://github.com/zjunlp/PromptKG/tree/main/research/Demo-Tuning.

Contrastive Demonstration Tuning for Pre-trained LLMs

The paper "Contrastive Demonstration Tuning for Pre-trained LLMs" presents an innovative approach for improving the performance of pre-trained LLMs (PLMs), especially in low-data scenarios, through a technique called contrastive demonstration tuning (Demo-tuning). This technique aims to optimize the demonstration component in prompt-tuning, an area less explored compared to other fine-tuning methodologies.

Overview

Pre-trained LLMs have become essential in NLP due to their ability to be fine-tuned for diverse tasks using textual prompts or demonstrations. Previous strategies have delved into discrete and continuous prompt optimization, but the demonstration sampling technique, which plays a crucial role in refining prompt-tuning performance, has not been thoroughly investigated. This paper proposes a novel method that leverages contrastive learning to enhance demonstration selection, improving the flexibility and efficiency of existing prompt-based methods.

Key Contributions

1. Pluggable and Extensible Approach: Demo-tuning is designed to be integrated into existing prompt-tuning methodologies without the need for manual demonstration sampling. This approach provides a platform to extend prompt-tuning to various classification tasks, regardless of the number of categories.
2. Virtual Demonstration with Contrastive Learning: By using continuous embeddings as virtual demonstrations, the method sidesteps the limitations imposed by model input length constraints. These virtual demonstrations are optimized through a straightforward contrastive framework that foregoes negative pairs, focusing instead on improving discriminative comparisons.
3. Comprehensive Evaluation: The authors conducted experiments across 16 NLP datasets, proving that their method achieves superior results when combined with established techniques like LM-BFF and P-tuning. Notably, in few-shot settings, Demo-tuning consistently outperformed standard fine-tuning and other prompt-based tuning methods.

Experimental Findings

The experimental results highlighted several advantages offered by Demo-tuning. For instance, significant improvements were observed in tasks like sentiment analysis and natural language inference when combined with P-tuning, showcasing its compatibility with different architectures. The authors also demonstrated the efficacy of virtual demonstrations in scenarios where the number of possible classes is extensive, overcoming the traditional limitations associated with input length in PLMs.

Additionally, alternative demonstration sampling strategies were evaluated. The use of contrastive learning to optimize virtual demonstrations proved more effective than both random and similarity-based sampling methods, suggesting a robust potential for this framework in enhancing NLP model performance.

Implications and Future Directions

From a practical perspective, Demo-tuning's flexibility and model-agnostic design imply its applicability across various NLP tasks without the need for extensive modifications to existing systems. The theoretical implications extend to possible connections with prototype learning, encouraging further investigation into the nature and role of demonstrations as prototypes within prompt-tuning frameworks.

Future research could explore parameter-efficient fine-tuning approaches leveraging this strategy, as well as applications beyond classification into generative tasks. Investigating the integration of external knowledge within demonstrations might also provide insights into the use of demonstrations as a means of knowledge enrichment in PLMs.

Conclusion

The paper presents a compelling case for contrastive demonstration tuning as a critical enhancement for pre-trained LLMs. Its potential to streamline prompt-based methods and improve performance in low-data scenarios makes it a valuable contribution to the field. Moving forward, understanding the broader applicability and optimization of virtual demonstrations within various architectures remains a fertile ground for research, promising advances in efficiency and effectiveness of NLP models.