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 introduce a new framework to describe request-making that segments user input into request content, roles assigned, query-specific context, and the remaining task-independent expressions. We apply the workflow to create and analyze a dataset of 211k real-world queries based on WildChat. Compared with similar human-human setups, we find significant differences in the language for request-making in the human-LLM scenario. Further, we introduce a novel and essential perspective of diachronic analyses with user expressions, which reveals fundamental and habitual user-LLM interaction patterns beyond individual task completion. We find that query patterns evolve from early ones emphasizing sole requests to combining more context later on, and individual users explore expression patterns but tend to converge with more experience. From there, we propose to understand communal trends of expressions underlying distinct tasks and discuss the preliminary findings. Finally, we discuss the key implications for user studies, computational pragmatics, and LLM alignment.

The term Language Models (LMs) as a time-specific collection of models of interest is constantly reinvented, with its referents updated much like the *Ship of Theseus* replaces its parts but remains the same ship in essence. In this paper, we investigate this *Ship of Language Models* problem, wherein scientific evolution takes the form of continuous, implicit retrofits of key *existing* terms. We seek to initiate a novel perspective of scientific progress, in addition to the more well-studied emergence of *new* terms. To this end, we construct the data infrastructure based on recent NLP publications. Then, we perform a series of text-based analyses toward a detailed, quantitative understanding of the use of Language Models as a term of art. Our work highlights how systems and theories influence each other in scientific discourse, and we call for attention to the transformation of this Ship that we all are contributing to.

Annotation quality is often framed as post-hoc cleanup of annotator-caused issues. This position paper discusses whether, how, and why this narrative limits the scope of improving annotation. We call to consider annotation as a procedural collaboration, outlining three points in this direction:(1) An issue can be either annotator- or researcher-oriented, where one party is accountable and the other party may lack ability to fix it; (2) yet, they can co-occur or have similar consequences, and thus any specific problem we encounter may be a combination;(3) therefore, we need a new language to capture the nuance and holistically describe the full procedure to resolve these issues.To that end, we propose to study how agency is manifested in annotation and picture how this perspective benefits the community more broadly.