565 citations · 653 across the 28 of their papers we have counts for
6 papers · 1 filter
Evaluating LLM Creativity through Long-Tail Performance
Yichen Wang, Tenghao Huang, Vishakh Padmakumar +1
Creativity benchmarks for large language models (LLMs) vary widely in task and metric design. This raises the question of whether they capture a common notion of creativity. We pro…
LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions
Myra Cheng, Lujain Ibrahim, Grace Liu +5
We characterize how people are turning to LLMs as oracles: all-knowing authorities on subjective personal questions. Motivated by risks to users' autonomy and well-being, we develo…
Offloading Score: Measuring AI Reliance Through Counterfactual Workflows
Vishakh Padmakumar, Lujain Ibrahim, Zora Zhiruo Wang +3
AI tools are increasingly integrated into real-world workflows. However, existing measures of reliance on these tools focus on AI output adoption or on self-reported indicators, ra…
SWE-chat: Coding Agent Interactions From Real Users in the Wild
Joachim Baumann, Vishakh Padmakumar, Xiang Li +3
AI coding agents are being adopted at scale, yet we lack empirical evidence on how people actually use them and how much of their output is useful in practice. We present SWE-chat,…
No Single Best Model for Diversity: Learning a Router for Sample Diversity
Yuhan Liu, Fangyuan Xu, Vishakh Padmakumar +2
When posed with prompts that permit a large number of valid answers, comprehensively generating them is the first step towards satisfying a wide range of users. In this paper, we s…
SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery
David Anugraha, Vishakh Padmakumar, Diyi Yang
Qualitative insights from user experiences are critical for informing product and policy decisions, but collecting such data at scale is constrained by the time and availability of…