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Phillip Howard

2 papers indexed

arxivcs.AIcs.CL2026-07-22

Refusal-Gated Decoding: Preserving Refusal Behavior Under High-Temperature Sampling

Phillip Howard, Xin Su, Allen Roush, Manikandan Ravikiran, Amir Abdullah

High-temperature sampling is one of the primary mechanisms for increasing diversity in LLMs. Recent advances in truncation-based sampling techniques have helped mitigate drawbacks of high-temperature sampling such as neural text degeneration, thereby enabling greater diversity in…

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arxivcs.CV2026-07-12

Improving Sample Diversity in Autoregressive Text-to-Image Generation via Cluster Truncation

Trang Nguyen, Shuang Wu, Runyan Tan, Phillip Howard

While diffusion models achieve state-of-the-art image quality for text-to-image (T2I) generation, recent work has demonstrated that they suffer from sample diversity collapse. In this work, we investigate whether autoregressive (AR) image generation models can push the Pareto fro…

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