Matryoshka: Learning to Drive Black-Box LLMs with LLMs

Abstract: Despite the impressive generative abilities of black-box LLMs, their inherent opacity hinders further advancements in capabilities such as reasoning, planning, and personalization. Existing works aim to enhance LLM capabilities via domain-specific adaptation or in-context learning, which require additional training on accessible model parameters, an infeasible option for black-box LLMs. To address this challenge, we introduce Matryoshika, a lightweight white-box LLM controller that guides a large-scale black-box LLM generator by decomposing complex tasks into a series of intermediate outputs. Specifically, we consider the black-box LLM as an environment, with Matryoshika serving as a policy to provide intermediate guidance through prompts for driving the black-box LLM. Matryoshika is trained to pivot the outputs of the black-box LLM aligning with preferences during iterative interaction, which enables controllable multi-turn generation and self-improvement in optimizing intermediate guidance. Empirical evaluations on three diverse tasks demonstrate that Matryoshika effectively enhances the capabilities of black-box LLMs in complex, long-horizon tasks, including reasoning, planning, and personalization. By leveraging this pioneering controller-generator framework to mitigate dependence on model parameters, Matryoshika provides a transparent and practical solution for improving black-box LLMs through controllable multi-turn generation using white-box LLMs.

• The paper introduces a controller-generator framework where a white-box LLM guides black-box outputs through iterative, intermediate prompts.
• It achieves measurable improvements with gains of 3.19% in reasoning, 7.46% in planning, and 5.82% in personalization accuracy.
• The framework offers scalable transparency and control, enabling enhanced interpretability and advanced AI solutions for complex tasks.

Overview of "Matryoshka: Learning to Drive Black-Box LLMs with LLMs"

The paper presents a novel framework called Matryoshka, designed to enhance the capabilities of black-box LLMs, particularly in tasks requiring nuanced reasoning, planning, and personalization. The framework achieves this by employing a lightweight, white-box LLM controller to guide a black-box LLM generator, thereby optimizing its output through intermediary guidance and iterative feedback.

Main Contributions and Methodology

Matryoshka introduces the concept of driving a black-box LLM using another LLM that acts as a controller. This controller is a white-box LLM, which allows for supervision and training adjustments not possible with opaque black-box models. The white-box model utilizes intermediate prompts or "guidance" which enhance the black-box LLM's ability to handle complex sequences or cognitive tasks. This novel controller-generator relationship allows Matryoshka to address the opacity challenge, typical in commercial black-box systems, by innovatively guiding generation through an accessible controller policy.

The research includes distinctive methodological elements:

1. Controller-Generator Framework: Matryoshka treats the black-box LLM as an environment influenced by the white-box controller. The system provides intermediate prompts to guide the black-box LLM during the generation process, effectively decomposing complex tasks into manageable steps.
2. Iterative Feedback and Optimization: A key aspect of Matryoshka is its ability to iteratively refine its guidance using feedback from the environment (i.e., the outputs and their evaluations). This interaction enables the controller to self-improve by learning from previous actions and refining future decisions.
3. Empirical Evaluations: The framework's efficacy is demonstrated across various tasks: personalization (LaMP), reasoning (GSM8K), and planning (ALFWorld). The results suggest marked improvement in black-box LLM performance, with considerable gains in reasoning, planning, and personalization capabilities without accessing or fine-tuning the underlying parameters of the black-box LLM.

Numerical Results and Claims

Matryoshka reports an average improvement of 3.19% in reasoning accuracy, 7.46% in the success rate for planning tasks, and 5.82% in personalization accuracy. These figures highlight the framework's effectiveness in enhancing LLM capabilities by leveraging structured interactions and task decomposition facilitated by the guiding white-box LLM.

Implications and Future Directions

The implications of Matryoshka's findings are multifaceted:

• Practical Applications: The ability to boost the effectiveness of black-box LLMs using a relatively small and adaptable controller offers scalable solutions in AI applications where model parameters are inaccessible or proprietary.
• Transparency and Control: By decoupling the task guidance from the opaque nature of black-box LLMs, Matryoshka provides a method to incorporate transparency and control in LLM-driven applications, a crucial advancement for industries relying on AI interpretability and compliance.
• Future Developments: The framework opens new avenues for integrating LLM-driven enhancements across complex, real-world AI applications, such as theorem proving and advanced software engineering, where long-horizon reasoning and planning are crucial. Subsequent research could focus on advanced controllers that further exploit Matryoshka’s architecture for broader, cross-domain applications.

Conclusion

The paper's contribution lies in its innovative framework that combines the strengths of both black and white-box LLMs, creating a symbiotic relationship that enhances the former's applicability in complex tasks without necessitating parameter retraining or direct access. This represents a significant step forward in leveraging LLM technologies for more nuanced and broad-scale AI applications.