1 citations · 1 across the 15 of their papers we have counts for
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Refusal-Gated Decoding: Preserving Refusal Behavior Under High-Temperature Sampling
Phillip Howard, Xin Su, Allen Roush +3
Recent advances in truncation-based sampling have helped mitigate drawbacks of high-temperature sampling such as neural text degeneration, thereby enabling greater diversity withou…
Synthetic Contrastive Reasoning for Multi-Table Q&A
Ankit Pratap Singh, Xin Su, Phillip Howard
Multi-table question answering requires models to retrieve relevant evidence, link schemas, and perform compositional reasoning across relational tables. Existing multi-table Q&A r…
Hybrid Verified Decoding: Learning to Allocate Verification in Speculative Decoding
Xin Su, Dawid Majchrowski, Fangyuan Yu +5
Large Language Model (LLM) generation remains expensive because autoregressive decoding calls the model once for each new token. Speculative decoding reduces this cost by drafting…
Geometry-Aware CLIP Retrieval via Local Cross-Modal Alignment and Steering
Nirmalendu Prakash, Narmeen Fatimah Oozeer, Xin Su +8
CLIP retrieval is typically framed as a pointwise similarity problem in a shared embedding space. While CLIP achieves strong global cross-modal alignment, many retrieval failures a…
MedThink: Enhancing Diagnostic Accuracy in Small Models via Teacher-Guided Reasoning Correction
Xinchun Su, Chunxu Luo, Lipeng Ma +2
Accurate clinical diagnosis requires extensive domain knowledge and complex clinical reasoning capabilities. Although large language models (LLMs) hold great potential for clinical…
Cross-Cultural Value Attribution in Large Vision-Language Models
Phillip Howard, Xin Su, Kathleen C. Fraser
The rapid adoption of large vision-language models (LVLMs) in recent years has been accompanied by growing fairness concerns due to their propensity to reinforce harmful societal s…
Cultural Counterfactuals: Evaluating Cultural Biases in Large Vision-Language Models with Counterfactual Examples
Phillip Howard, Xin Su, Kathleen C. Fraser
Large Vision-Language Models (LVLMs) have grown increasingly powerful in recent years, but can also exhibit harmful biases. Prior studies investigating such biases have primarily f…