468 citations · 527 across the 16 of their papers we have counts for
17 papers
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…
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…
Identifying Task Groupings for Multi-Task Learning Using Pointwise V-Usable Information
Yingya Li, Timothy Miller, Steven Bethard +1
The success of multi-task learning can depend heavily on which tasks are grouped together. Naively grouping all tasks or a random set of tasks can result in negative transfer, with…
Memorization in In-Context Learning
Shahriar Golchin, Mihai Surdeanu, Steven Bethard +2
In-context learning (ICL) has proven to be an effective strategy for improving the performance of large language models (LLMs) with no additional training. However, the exact mecha…
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…