Cascaded Batch Prompting
Although batch prompting makes large language model inference more efficient by processing multiple instances simultaneously, it suffers from unpredictable downstream task performance. We propose cascaded batch prompting, a two-stage approach designed to resolve the unpredictability of conventional batch prompting by disentangling complex reasoning from symbol grounding. Experiments on multiple-choice question answering and natural language inference demonstrate that the proposed method outperforms the standard single prompting baseline while achieving a speedup proportional to batch size, establishing a new state of the art on the Pareto frontier.
| Authors | Sho Hoshino, Peinan Zhang |
|---|---|
| Primary category | cs.CL |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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