Towards a Small Language Model Lifecycle Framework

Benchmark suites such as MMLU and HellaSwag measure core capabilities but are vulnerable to data contamination, making careful curation and transparent reporting essential [OS21], [OS2], [OS13], [OS6]. Trustworthiness evaluation covers robustness to adversarial inputs, privacy protection, reliability (including hallucination and consistency), and safety concerns such as toxicity and bias [OS2], [OS6], all of which are vital for user-facing or high-stakes deployments. Resource efficiency--spanning computational cost, memory, energy, and deployment overhead--is particularly important for SLMs and shapes deployment strategies in constrained environments [OS5], [OS6]. Automated evaluation methods range from statistical scorers like BLEU and ROUGE to model-based and hybrid approaches, with the latter providing stronger alignment with human judgment and greater scalability [OS29], [OS30]. Ultimately, evaluation should be an integrated, continuous process that informs model iteration, balances performance with sustainability and safety, and supports real-world usability at scale.