Firmware reverse engineering is essential for uncovering internal mechanisms and identifying security vulnerabilities. While restoring the structural elements of code is achievable, the absence of function names significantly complicates the process of analyzing and understanding firmware logic. Recognizing the capabilities of large language models (LLMs) in code generation, this paper explores their potential in automating the renaming of functions. We introduce FirmNamer, a prototype system designed to streamline the labor-intensive task of analyzing decompiled code and assigning logical function names. FirmNamer achieves this by constructing dynamic prompts for LLMs based on extracted function code and contextual information. Through extensive evaluation, FirmNamer demonstrates superior performance in function renaming, achieving functional precision and semantic precision of 86.6% and 49%, respectively, outperforming existing state-of-the-art approaches such as DeGPT, DEBIN, NFRE, NERO, and SYMLM.