1 citations · 1 across the 17 of their papers we have counts for
18 papers
CoGate: Confidence-Gated Co-Decoding for Secure Code Generation
Minghao Hu, Lannan Luo, Allen Roush +1
Large language models are widely used for code generation, but they can also produce insecure programs due to patterns learned from their pretraining data. Decoding-time steering h…
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…
Improving Sample Diversity in Autoregressive Text-to-Image Generation via Cluster Truncation
Trang Nguyen, Shuang Wu, Runyan Tan +1
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 t…
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…
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…
p-less Sampling: A Robust Hyperparameter-Free Approach for LLM Decoding
Runyan Tan, Shuang Wu, Phillip Howard
Obtaining high-quality outputs from Large Language Models (LLMs) often depends upon the choice of a sampling-based decoding strategy to probabilistically choose the next token at e…
Investigating the Robustness of Retrieval-Augmented Generation at the Query Level
Sezen Perçin, Xin Su, Qutub Sha Syed +4
Large language models (LLMs) are very costly and inefficient to update with new information. To address this limitation, retrieval-augmented generation (RAG) has been proposed as a…
A Semantic Parsing Framework for End-to-End Time Normalization
Xin Su, Sungduk Yu, Phillip Howard +1
Time normalization is the task of converting natural language temporal expressions into machine-readable representations. It underpins many downstream applications in information r…
DPO Learning with LLMs-Judge Signal for Computer Use Agents
Man Luo, David Cobbley, Xin Su +4
Computer use agents (CUA) are systems that automatically interact with graphical user interfaces (GUIs) to complete tasks. CUA have made significant progress with the advent of lar…
Transformer-Based Temporal Information Extraction and Application: A Review
Xin Su, Phillip Howard, Steven Bethard
Temporal information extraction (IE) aims to extract structured temporal information from unstructured text, thereby uncovering the implicit timelines within. This technique is app…
Training-Free Mitigation of Language Reasoning Degradation After Multimodal Instruction Tuning
Neale Ratzlaff, Man Luo, Xin Su +2
Multimodal models typically combine a powerful large language model (LLM) with a vision encoder and are then trained on multimodal data via instruction tuning. While this process a…
Scaling Knowledge Graph Construction through Synthetic Data Generation and Distillation
Prafulla Kumar Choubey, Xin Su, Man Luo +9
Document-level knowledge graph (KG) construction faces a fundamental scaling challenge: existing methods either rely on expensive large language models (LLMs), making them economic…
SK-VQA: Synthetic Knowledge Generation at Scale for Training Context-Augmented Multimodal LLMs
Xin Su, Man Luo, Kris W Pan +3
Multimodal retrieval augmented generation (RAG) plays a crucial role in domains such as knowledge-based visual question answering (KB-VQA), where external knowledge is needed to an…
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning
Xin Su, Tiep Le, Steven Bethard +1
An important open question in the use of large language models for knowledge-intensive tasks is how to effectively integrate knowledge from three sources: the model's parametric me…
Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering
Xin Su, Phillip Howard, Nagib Hakim +1
Answering time-sensitive questions from long documents requires temporal reasoning over the times in questions and documents. An important open question is whether large language m…