5 papers
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…