arXiv:2602.15894v2 Announce Type: replace-cross Abstract: In many large language model (LLM) alignment applications, users expect not only high-quality outputs but also substantial diversity. However, existing methods often face a fundamental trade-off between these objectives: approaches that improve output quality tend to reduce diversity, while methods that increase diversity often do so at the expense of quality. In this work, we propose Quality-constrained Entropy Maximization Policy Optimization (QEMPO), a novel framework that enhances the diversity of LLM outputs while explicitly preser
The increasing deployment of LLMs across diverse applications highlights the fundamental tension between output quality and diversity, driving research into novel optimization frameworks.
Improving LLM diversity without sacrificing quality is crucial for enhancing user experience, robustness in varied applications, and mitigating biases in AI systems.
New policy optimization methods that explicitly constrain quality while maximizing diversity could lead to more nuanced and flexible LLM deployments.
- · LLM developers
- · AI product companies
- · Users of generative AI
- · Companies relying on monolithic, undiversified LLM outputs
- · AI models prone to mode collapse
Wider adoption and applicability of LLMs in specialized and creative domains due to improved output diversity.
Reduced need for extensive fine-tuning or post-processing to achieve diverse outputs, streamlining development workflows.
Enhanced trust and ethical alignment of LLMs as they demonstrate a broader range of responses, potentially mitigating societal risks associated with narrow AI outputs.
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Read at arXiv cs.LG