One Model, Many Skills: Parameter-Efficient Fine-Tuning for Multitask Code Analysis

ArXi:2603.09978v1 Announce Type: cross Large language models have recently surpassed specialized systems on code generation, yet their effectiveness on other code-analysis tasks remains less clear. At the same time, multi-task learning offers a way to unify diverse objectives within a single model, but fully fine-tuning LLMs across tasks is computationally prohibitive. Parameter-efficient fine-tuning mitigates this cost by updating only a small fraction of weights.