  The rapid advancement of large language models has given rise to a plethora
of applications across a myriad of real-world tasks, mainly centered on
aligning with human intent. However, the complexities inherent in human intent
necessitate a dependence on labor-intensive and time-consuming human
evaluation. To alleviate this constraint, we delve into the paradigm of
employing open-source large language models as evaluators, aligning with the
prevailing trend of utilizing GPT-4. Particularly, we present a step-by-step
evaluation framework: \textbf{Fennec}, capable of \textbf{F}ine-grained
\textbf{E}valuatio\textbf{N} and correctio\textbf{N} \textbf{E}xtended through
bran\textbf{C}hing and bridging. Specifically, the branching operation dissects
the evaluation task into various dimensions and granularities, thereby
alleviating the challenges associated with evaluation. Concurrently, the
bridging operation amalgamates diverse training datasets, augmenting the
variety of evaluation tasks. In experimental trials, our 7B model consistently
outperforms open-source larger-scale evaluation models across various widely
adopted benchmarks in terms of both \textit{Agreement} and
\textit{Consistency}, closely approaching the capabilities of GPT-4. We employ
the fine-grained correction capabilities induced by the evaluation model to
refine multiple model responses, and the results show that the refinement
elevates the quality of responses, leading to an improvement of 1-2 points on
the MT-Bench. Our code is available at
Github\footnote{\url{https://github.com/dropreg/Fennec}}.
