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Found 11 papers, 2 papers with code
The Use of Generative Search Engines for Knowledge Work and Complex Tasks
Until recently, search engines were the predominant method for people to access online information.
Advancing the Search Frontier with AI Agents
As many of us in the information retrieval (IR) research community know and appreciate, search is far from being a solved problem.
Making Large Language Models Better Data Creators
In our experiments we demonstrate that instruction-following LLMs are highly cost-effective data creators, and that models trained with these data exhibit performance better than those trained with human-labeled data (by up to 17. 5%) on out-of-distribution evaluation, while maintaining comparable performance on in-distribution tasks.
Ranked #3 on
Visual Question Answering
on ViP-Bench
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
However, using LLMs to generate a user intent taxonomy and apply it for log analysis can be problematic for two main reasons: (1) such a taxonomy is not externally validated; and (2) there may be an undesirable feedback loop.
Taking Search to Task
For decades, scholars made a case for the role that a user's task plays in how and why that user engages in search and what a search system should do to assist.
MS-LaTTE: A Dataset of Where and When To-do Tasks are Completed
Tasks are a fundamental unit of work in the daily lives of people, who are increasingly using digital means to keep track of, organize, triage and act on them.
CoSEM: Contextual and Semantic Embedding for App Usage Prediction
App usage prediction is important for smartphone system optimization to enhance user experience.
Formation of Social Ties Influences Food Choice: A Campus-Wide Longitudinal Study
To estimate causal effects from the passively observed log data, we control confounds in a matched quasi-experimental design: we identify focal users who at first do not have any regular eating partners but then start eating with a fixed partner regularly, and we match focal users into comparison pairs such that paired users are nearly identical with respect to covariates measured before acquiring the partner, where the two focal users' new eating partners diverge in the healthiness of their respective food choice.
Population-Scale Study of Human Needs During the COVID-19 Pandemic: Analysis and Implications
Most work to date on mitigating the COVID-19 pandemic is focused urgently on biomedicine and epidemiology.
Optimizing Interactive Systems via Data-Driven Objectives
Effective optimization is essential for real-world interactive systems to provide a satisfactory user experience in response to changing user behavior.
Conversations with Documents. An Exploration of Document-Centered Assistance
Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario.
