From the 1 of 11 linked papers with an AI index.
1 citations · 1 across the 4 of their papers we have counts for
11 papers
Don't Repeat Yourself: Stopping Verbatim Loops at Sampling Time
Philipp Emanuel Weidmann, Allen Roush, Judah Goldfeder +2
Large Language Models generate text autoregressively, but open-ended generation is prone to verbatim looping, in which models repeat spans already present in context. Standard defe…
XTC: Head-Aware Sampling by Excluding Top Choices
Philipp Emanuel Weidmann, Allen Roush, Judah Goldfeder +2
Standard decoding rules for autoregressive language models promote diversity by rescaling the full next-token distribution or truncating its low-probability tail. These strategies…
CoGate: Confidence-Gated Co-Decoding for Secure Code Generation
Minghao Hu, Lannan Luo, Allen Roush +1
The paper introduces CoGate, a method that uses the confidence of a security expert model to gate its influence during co-decoding for generating more secure code with large langua…
Refusal-Gated Decoding: Preserving Refusal Behavior Under High-Temperature Sampling
Phillip Howard, Xin Su, Allen Roush +2
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…
Mirage Probes: How Vision Models Fake Visual Understanding
Daniel Ben-Levi, Judah Goldfeder, Weiliang Zhao +5
Vision-language models (VLMs) can answer image-based questions confidently, and often correctly, even when no image is provided. This mirage behavior inflates benchmark scores with…
Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests
Alexandra Yost, Shreyans Jain, Shivam Raval +6
Persona conditioning is widely used to steer large language model (LLM) behavior, but it is unclear whether it induces stable behavioral structure or superficial variation. We prop…