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The dominance of proprietary LLMs has led to restricted access and raisedinformation privacy concerns. High-performing open-source alternatives arecrucial for information-sensitive and high-volume applications but often lagbehind in performance. To address this gap, we propose (1) A untargeted variantof iterative self-critique and self-refinement devoid of external influence.(2) A novel ranking metric - Performance, Refinement, and Inference Cost Score(PeRFICS) - to find the optimal model for a given task considering refinedperformance and cost. Our experiments show that SoTA open source models ofvarying sizes from 7B - 65B, on average, improve 8.2% from their baselineperformance. Strikingly, even models with extremely small memory footprints,such as Vicuna-7B, show a 11.74% improvement overall and up to a 25.39%improvement in high-creativity, open ended tasks on the Vicuna benchmark.Vicuna-13B takes it a step further and outperforms ChatGPT post-refinement.This work has profound implications for resource-constrained andinformation-sensitive environments seeking to leverage LLMs without incurringprohibitive costs, compromising on performance and privacy. The domain-agnosticself-refinement process coupled with our novel ranking metric facilitatesinformed decision-making in model selection, thereby reducing costs anddemocratizing access to high-performing language models, as evidenced by casestudies.

Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models