Where You Inject Diversity Matters: A Unified Framework for Diverse Generation

2026-06-08

AI Summary

Large language models often produce semantically homogeneous outputs in open-ended generation tasks, failing to meet diversity requirements. This work proposes a unified framework that, for the first time, systematically characterizes the design space of test-time diversity methods by automatically injecting controllable diversity into an intermediate latent representation and conditioning final response generation on this diverse representation. The approach integrates representation-level guidance, conditional language modeling, and a transfer score—quantifying the influence of source diversity on model outputs—for joint optimization. Experimental results across five open-ended tasks and four backbone architectures demonstrate that the proposed framework substantially enhances output diversity while maintaining generation quality on par with baseline models.

Abstract

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 diverse intermediate specifications and then condition on them to produce final responses. Across five open-ended tasks and four backbone models, specification-level injection improves output diversity over test-time baselines while maintaining comparable quality. Our analysis shows that successful diversity injection depends on both the diversity of the sources and their transmission to the output, highlighting source design and source-to-output realization as two key levers for building more diverse generation systems.

Problem

• diverse generation
• large language models
• open-ended generation
• test-time diversity
• output diversity

Innovation

• diverse generation
• specification-level injection
• transmission score
• test-time diversity
• intermediate specifications