Where You Inject Diversity Matters: A Unified Framework for Diverse Generation
Open-ended generation tasks often require a set of meaningfully different outputs, yet large language models often produce similar generations. Existing test-time diversity methods operate at different stages of generation with varying effectiveness, but it remains unclear what design choices lead to meaningful diversity in the output. We introduce a framework that characterizes test-time diverse generation methods by the diversity source introduced during generation and provide a transmission score for measuring how effectively variation in the source reaches the final output. Guided by this framework, we propose fully automated specification-level generation methods that first generate ...
| Authors | Cheng Zhang, Rui Xin, Chudi Zhong |
|---|---|
| Primary category | cs.CL |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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