1 citations · 1 across the 17 of their papers we have counts for
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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…