20 citations · 70 across the 17 of their papers we have counts for
6 papers · 1 filter
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques
Daking Rai, Bailin Wang, Yilun Zhou +1
Compositional and domain generalization present significant challenges in semantic parsing, even for state-of-the-art semantic parsers based on pre-trained language models (LMs). I…
Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)
Daking Rai, Yilun Zhou, Bailin Wang +1
While large language models (LLMs) have demonstrated strong capability in structured prediction tasks such as semantic parsing, few amounts of research have explored the underlying…
ExSum: From Local Explanations to Model Understanding
Yilun Zhou, Marco Tulio Ribeiro, Julie Shah
Interpretability methods are developed to understand the working mechanisms of black-box models, which is crucial to their responsible deployment. Fulfilling this goal requires bot…
The Irrationality of Neural Rationale Models
Yiming Zheng, Serena Booth, Julie Shah +1
Neural rationale models are popular for interpretable predictions of NLP tasks. In these, a selector extracts segments of the input text, called rationales, and passes these segmen…
Learning Household Task Knowledge from WikiHow Descriptions
Yilun Zhou, Julie A. Shah, Steven Schockaert
Commonsense procedural knowledge is important for AI agents and robots that operate in a human environment. While previous attempts at constructing procedural knowledge are mostly…
Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness
Yilun Zhou, Steven Schockaert, Julie A. Shah
In many applications, it is important to characterize the way in which two concepts are semantically related. Knowledge graphs such as ConceptNet provide a rich source of informati…