  Despite the strong performance of large language models (LLMs) across a wide
range of tasks, they still have reliability issues. Previous studies indicate
that strong LLMs like GPT-4-turbo excel in evaluating the reliability of
responses from LLMs, but face efficiency and local deployment issues. Thus, to
enable weak LLMs to effectively assess the reliability of LLM responses, we
propose a novel cross-query-comparison-based method called $\textit{Meta
Ranking}$ (MR). Unlike previous few-shot methods that solely based on
in-context learning capabilities in LLMs, MR assesses reliability by pairwisely
ranking the target query-response pair with multiple reference query-response
pairs. We found that MR is highly effective in error detection for LLM
responses, where weak LLMs, such as Phi-2, could surpass strong baselines like
GPT-3.5-turbo, requiring only five reference samples and significantly
improving efficiency. We further demonstrate that MR can enhance strong LLMs'
performance in two practical applications: model cascading and instruction
tuning. In model cascading, we combine open- and closed-source LLMs to achieve
performance comparable to GPT-4-turbo with lower costs. In instruction tuning,
we use MR for iterative training data filtering, significantly reducing data
processing time and enabling LLaMA-7B and Phi-2 to surpass Alpaca-13B with
fewer training tokens. These results underscore the high potential of MR in
both efficiency and effectiveness.
