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This research introduces cascaded batch prompting, a two-stage technique that disentangles complex reasoning from symbol grounding to stabilize performance in large language model inference. By processing multiple instances simultaneously while maintaining accuracy, the method achieves significant speedups proportional to batch size across multiple-choice and natural language inference tasks. This advancement directly addresses the unpredictability of conventional batch prompting, offering a more efficient path toward scaling LLM operations without sacrificing downstream task quality.
Cascaded Batch Prompting
arXiv:2608.27038v1 Announce Type: new Abstract: 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.
Abstract: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.
Computation and Language (cs.CL)
arXiv:2608.27038 [cs.CL]
(or arXiv:2608.27038v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.27038 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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