2 citations · 4 across the 6 of their papers we have counts for
8 papers
Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows
Shirley Anugrah Hayati, Patricia Anugrah Setiani, Ruizi Wang +4
When humans collaborate with AI agents on long-horizon tasks, what happens to the agents' contributions? For solving long-horizon tasks, humans place greater importance on ongoing…
Orchestrating GenAI for Interdisciplinary Research
Shirley Anugrah Hayati, Moyan Zhou, Patricia Anugrah Setiani +3
As researchers tackle interdisciplinary problems, they face the need to deepen expertise in primary areas while rapidly acquiring knowledge in secondary domains. Generative AI (Gen…
Effects of Varying LLM Access on Essay Writing Behavior
Julia Christenson, Karin de Langis, Shirley Anugrah Hayati +1
Investigating the degree to which large language models (LLMs) affect teaching and learning in universities can help identify strategies for integrating LLMs in a way that supports…
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Ofer Meshi, Krisztian Balog, Sally Goldman +5
The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…
Are LLM Agents Behaviorally Coherent? A Latent Profiling Approach for Social Simulation
James Mooney, Josef Woldense, Zheng Robert Jia +4
LLM agents are increasingly proposed as substitutes for human participants in social research, usually on the strength of survey responses that match those of the humans they repre…
Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation
Ruixin Yang, Dheeraj Rajagopal, Shirley Anugrah Hayati +2
Uncertainty estimation is a significant issue for current large language models (LLMs) that are generally poorly calibrated and over-confident, especially with reinforcement learni…
Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models
Shirley Anugrah Hayati, Taehee Jung, Tristan Bodding-Long +4
Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks. Howe…
Under the Surface: Tracking the Artifactuality of LLM-Generated Data
Debarati Das, Karin De Langis, Anna Martin-Boyle +14
This work delves into the expanding role of large language models (LLMs) in generating artificial data. LLMs are increasingly employed to create a variety of outputs, including ann…