The rapid evolution of large language models (LLMs) has revolutionized natural language processing (NLP) tasks such as text generation, translation, and comprehension. However, the increasing computational demands and inference costs of these models present significant challenges. This study investigates the dynamic and efficient utilization of pre-trained weights from open-sourced LLMs of varying parameter sizes to achieve an optimal balance between computational efficiency and task performance. Drawing inspiration from the dual-process theory of human cognition, we introduce StitchLLM: a dynamic model routing framework that employs a powerful bottom model to process all queries, and uses a lightweight routing mechanism to allocate computational resources appropriately. Our novel framework optimizes efficiency and maintains performance, leveraging a trainable stitching layer for seamless integration of decoder layers across different LLMs. Experimental results demonstrate that StitchLLM improves system throughput while minimizing performance degradation, offering a flexible solution for deploying LLMs in resource-constrained settings.

In this paper, we introduce a novel approach for enhancing the reasoning capabilities of large language models (LLMs) for constraint satisfaction problems (CSPs), by converting reasoning problems into classification tasks. Our method leverages the LLM’s ability to decide when to call a function from a set of logical-linguistic primitives, each of which can interact with a local “scratchpad” memory and logical inference engine. Invocation of these primitives in the correct order writes the constraints to the scratchpad memory and enables the logical engine to verifiably solve the problem. We additionally propose a formal framework for exploring the “linguistic” hardness of CSP reasoning-problems for LLMs. Our experimental results demonstrate that under our proposed method, tasks with significant computational hardness can be converted to a form that is easier for LLMs to solve and yields a 40% improvement over baselines. This opens up new avenues for future research into hybrid cognitive models that integrate symbolic and neural approaches.