Privacy-preserved LLM Cascade via CoT-enhanced Policy Learning

Abstract: LLMs have gained significant attention in on-device applications due to their remarkable performance across real-world tasks. However, on-device LLMs often suffer from suboptimal performance due to hardware limitations. A promising solution to this challenge is cascading a weaker local (on-device) LLM with a more powerful server LLM. While existing research on LLM cascade primarily optimizes the performance-cost trade-off, real-world applications impose additional requirements, such as privacy preservation, which remain largely unaddressed. In this work, we move beyond existing confidence- and logit-based LLM cascade methods and propose $\mathbf{P^{3}Defer}$, a novel Chain-of-Thought (CoT)-enhanced \textbf{p}olicy learning framework for \textbf{p}rivacy-\textbf{p}reserved \textbf{defer}ral decision-making. Our approach effectively improves cascade efficiency while mitigating privacy risks. Extensive experiments on three benchmark datasets demonstrate the effectiveness and superiority of $\mathbf{P^{3}Defer}$ over existing methods.

• The paper introduces a novel multi-objective optimization framework that balances performance, cost, and privacy in LLM cascades.
• It employs both training-free techniques, like prompt engineering, and training-based methods including instruction and loss tuning to optimize deferral decisions.
• Experimental results on GSM8K, MedQSum, and WMT22 benchmarks demonstrate improved local model accuracy and reduced server call rates.

LLM Cascade with Multi-Objective Optimal Consideration

The paper "LLM Cascade with Multi-Objective Optimal Consideration" addresses an important concern in the deployment of LLMs: the balance of performance, cost, and additional real-world requirements, such as privacy. The authors propose an innovative approach to LLM cascading, introducing a system that considers multiple objectives beyond the conventional cost-performance trade-off.

Overview of the Methodology

The primary innovation is the multi-objective optimization of LLM cascades. This advancement accounts for diverse considerations like privacy to align with real-world applications. The method involves a local LLM providing initial responses, and a deferral module decides whether to engage a more resource-intensive server model based on these responses, taking into account multiple considerations. The paper employs both training-free techniques, such as prompt engineering, and training-based methods, including instruction and loss tuning, to optimize the deferral decisions.

Key Contributions

1. Expansion of Cascade Objectives: The research extends the LLM cascading framework by incorporating additional objectives, thereby improving the alignment with application-specific requirements.
2. Training and Training-Free Methods: By examining both training-based (instruction tuning, loss tuning) and training-free (prompt engineering) methods, the paper provides a comprehensive toolkit for enhancing local LLMs' understanding of cascade logic.
3. Empirical Validation: Through rigorous experiments on three benchmarks—GSM8K for mathematical problem solving, MedQSum for medical questions, and WMT22 for translation—the approach demonstrated notable improvements in both performance and privacy awareness.

Experimental Outcomes

The experiments reveal that incorporating multi-objective optimizations into the cascade framework significantly improves local model performance while effectively managing privacy concerns. For instance, using the GSM8K dataset, the loss-tuned local LLM achieved a 55.92% accuracy, surpassing the server model's performance by 3.07% with an 81.2% call rate. Notably, training-based methods provided superior performance at a reduced server call rate compared to prompt engineering alone. Additionally, the inclusion of privacy considerations resulted in more judicious routing of queries to the server, minimizing privacy leaks.

Implications and Future Directions

The study provides a robust framework for integrating multi-objective considerations into LLM cascading, with significant implications for optimizing the deployment of LLMs in privacy-sensitive and cost-constrained environments. Practically, this approach can support more adaptive and secure implementations of LLMs across various applications. Theoretically, the work offers a new perspective on model cascading, encouraging further research into more advanced multi-objective optimization techniques.

Future research could explore additional objectives that further complicate the cascade decision-making process, such as ethical constraints or real-time responsiveness. Moreover, extending the framework to support more complex interactions between objectives could yield further performance improvements. The consideration of memory-enhanced models might also provide a pathway to maintaining high performance while minimizing resource usage.

In conclusion, this paper significantly contributes to the field by broadening the scope of LLM cascades to encompass real-world complexities alongside traditional cost-performance metrics. As AI continues to be integrated into sensitive areas, such as healthcare and finance, these advancements will become increasingly vital.