Chain2Parallel: Post-Hoc Parallelization of T5 Classifier Chains for Few-Shot Multi-Label Classification

Multi-label text classification (MLTC) aims to assign a set of correlated labels to each document, a task that becomes particularly challenging when labeled data is scarce. Autoregressive classifier chains model these dependencies effectively, but their sequential label decoding incurs substantial inference cost. We propose Chain2Parallel, a post-hoc parallelization method that transforms a completed, task-specialized T5 Chain into an efficient, single-pass classifier. By leveraging the frozen label-conditioned encoder and extracting its label-aligned sentinel representations, Chain2Parallel predicts all labels in parallel via a lightweight shared classification head. Across three few-shot benchmarks, Chain2Parallel matches or improves upon the original chain's classification performance while substantially reducing inference latency, memory consumption, and model footprint.