[Context and Motivation] Online user feedback provides valuable information to support requirements engineering (RE). How- ever, analysing online user feedback is challenging due to its large vol- ume and noise. Large language models (LLMs) show strong potential to automate this process and outperform previous techniques. They can also enable new tasks, such as generating requirements specifications. [Question/Problem] Despite their potential, the use of LLMs to anal- yse user feedback for RE remains underexplored. Existing studies offer limited empirical evidence, lack thorough evaluation, and rarely provide replication packages, undermining validity and reproducibility. [Princi- pal Idea/Results] We evaluate five lightweight open-source LLMs on three RE tasks: user request classification, NFR classification, and re- quirements specification generation. Classification performance was mea- sured on two feedback datasets, and specification quality via human evaluation. LLMs achieved moderate-to-high classification accuracy (F1 ≈0.47–0.68) and moderately high specification quality (mean ≈3/5). [Contributions]WenewlyexplorelightweightLLMsforfeedback-driven requirements development. Our contributions are: (i) an empirical eval- uation of lightweight LLMs on three RE tasks, (ii) a replication package, and (iii) insights into their capabilities and limitations for RE.