Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning (2310.12774v1)

Abstract: Prompt-based learning has been an effective paradigm for large pretrained LLMs (LLM), enabling few-shot or even zero-shot learning. Black-box prompt search has received growing interest recently for its distinctive properties of gradient-free optimization, proven particularly useful and powerful for model-as-a-service usage. However, the discrete nature and the complexity of combinatorial optimization hinder the efficiency of modern black-box approaches. Despite extensive research on search algorithms, the crucial aspect of search space design and optimization has been largely overlooked. In this paper, we first conduct a sensitivity analysis by prompting LLM, revealing that only a small number of tokens exert a disproportionate amount of influence on LLM predictions. Leveraging this insight, we propose the Clustering and Pruning for Efficient Black-box Prompt Search (ClaPS), a simple black-box search method that first clusters and prunes the search space to focus exclusively on influential prompt tokens. By employing even simple search methods within the pruned search space, ClaPS achieves state-of-the-art performance across various tasks and LLMs, surpassing the performance of complex approaches while significantly reducing search costs. Our findings underscore the critical role of search space design and optimization in enhancing both the usefulness and the efficiency of black-box prompt-based learning.

Overview of "Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning"

The paper "Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning" addresses a key challenge in prompt-based learning for LLMs: the efficiency of black-box prompt search. Existing methods for prompt optimization suffer from inefficiencies due to the discrete and combinatorial nature of the search space. This paper introduces an innovative methodology, Clustering and Pruning for Efficient Black-box Prompt Search (ClaPS), to improve the efficacy and efficiency of prompt optimization under a black-box setup where gradient information is unavailable.

Key Contributions

1. Sensitivity Analysis: The authors conduct a sensitivity analysis to understand the influence of individual tokens on LLM predictions. They reveal that a small subset of tokens has a significant impact on model output, suggesting that most tokens in the search space are either redundant or detrimental.
2. ClaPS Methodology: Building upon this sensitivity analysis, the ClaPS algorithm is developed. This method initially clusters and prunes the token search space to focus solely on influential tokens. This refined search space enables simpler search strategies to achieve state-of-the-art performance more efficiently.
3. Performance Evaluation: The paper reports that even simple search approaches, when combined with the optimized search space from ClaPS, outperform more complex counterparts in various tasks, while dramatically reducing computational costs.

Implications

Theoretical Implications

The analysis of token influence within LLMs adds a novel dimension to the understanding of model interpretability. The discovery that only a limited number of tokens drive the predictive capability of LLMs raises questions about the current practices in prompt-based learning and optimization. This work invites further exploration into whether similar phenomena occur in other types of model architectures or NLP tasks.

By reframing the problem space, this research lays the groundwork for developing more streamlined and efficient methods for interacting with large-scale models. The ClaPS strategy's effectiveness in simplifying the search space could inspire similar approaches in related areas such as neural architecture search and hyperparameter tuning.

Practical Implications

In practice, applying ClaPS can lead to substantial resource savings when deploying LLMs, especially in model-as-a-service scenarios where access to model parameters is restricted, and computational resources are at a premium. For tasks that utilize LLMs in a few-shot or zero-shot learning context, the reduced computational burden translates to quicker iterations and potentially lower costs.

Practitioners designing products or services around LLMs could leverage the insights from this paper to enhance user experience by deriving more effective prompts with lower latency and cost.

Future Developments

The research opens several avenues for future investigation, particularly in extending the ClaPS methodology to other domains and types of models. Exploring whether clustering and pruning strategies can be generalized across various model sizes and architectures could provide broader utility. Additionally, integrating ClaPS with other state-of-the-art optimization techniques could produce synergies leading to even further enhancements in efficiency and performance.

Innovations in search space design, as highlighted by ClaPS, could reshape the landscape of prompt-based learning, emphasizing the importance of search space reduction in future AI system optimization.

In summary, this paper makes an important contribution to the field of prompt-based learning, offering a novel method that combines theoretical insight with practical utility. Its approach to optimizing black-box prompt search presents a new direction that balances effectiveness with efficiency, providing potential benefits across both academic research and real-world applications.