5 citations · 6 across the 5 of their papers we have counts for
6 papers
Priming, Path-dependence, and Plasticity: Understanding the molding of user-LLM interaction and its implications from (many) chat logs in the wild
Shengqi Zhu, Jeffrey M. Rzeszotarski, David Mimno
User interactions with LLMs are shaped by prior experiences and individual exploration, but in-lab studies do not provide system designers with visibility into these in-the-wild fa…
Show or Tell? Modeling the evolution of request-making in Human-LLM conversations
Shengqi Zhu, Jeffrey M. Rzeszotarski, David Mimno
Designing user-centered LLM systems requires understanding how people use them, but patterns of user behavior are often masked by the variability of queries. In this work, we intro…
What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Models
Shengqi Zhu, Jeffrey M. Rzeszotarski
The term Language Models (LMs) as a time-specific collection of models of interest is constantly reinvented, with its referents updated much like the rep…
Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED
Quzhe Huang, Shibo Hao, Yuan Ye +3
DocRED is a widely used dataset for document-level relation extraction. In the large-scale annotation, a \textit{recommend-revise} scheme is adopted to reduce the workload. Within…
Exploring Distantly-Labeled Rationales in Neural Network Models
Quzhe Huang, Shengqi Zhu, Yansong Feng +1
Recent studies strive to incorporate various human rationales into neural networks to improve model performance, but few pay attention to the quality of the rationales. Most existi…
Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction
Quzhe Huang, Shengqi Zhu, Yansong Feng +3
Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a smal…