523 citations · 584 across the 20 of their papers we have counts for
6 papers · 1 filter
Mosaic: Runtime-Efficient Multi-Agent Embodied Planning
Kunjal Panchal, Saayan Mitra, Sunav Choudhary +3
LLM-based multi-agent embodied planning remains impractical due to prohibitively high execution latency. We identify failed actions as the dominant bottleneck, stemming from two co…
TACO: Task-Aware Column Description Generation Using LLMs
Ting Cai, Rakesh R. Menon, Yiru Chen +8
Generating accurate and informative column descriptions (e.g. "membership status of customers" for the column name "cust_mem") is essential for a wide range of downstream NLP tasks…
Improving Selective Classification with Pairwise Queries for Binary Classification
Harsh Vardhan, Sunav Choudhary, Natwar Modani +1
In selective classification, a model predicts the labels of data samples where it is confident, and abstains from predicting labels for samples on which it is not confident. The re…
TeamFusion: Supporting Open-ended Teamwork with Multi-Agent Systems
Jiale Liu, Victor S. Bursztyn, Lin Ai +4
In open-ended domains, teams must reconcile diverse viewpoints to produce strong deliverables. Answer aggregation approaches commonly used in closed domains are ill-suited to this…
Learning to Reason in LLMs by Expectation Maximization
Junghyun Lee, Branislav Kveton, Anup Rao +4
Large language models (LLMs) solve reasoning problems by first generating a rationale and then answering. We formalize reasoning as a latent variable model and derive a reward-base…
PRISM: Learning Design Knowledge from Data for Stylistic Design Improvement
Huaxiaoyue Wang, Sunav Choudhary, Franck Dernoncourt +2
Graphic design often involves exploring different stylistic directions, which can be time-consuming for non-experts. We address this problem of stylistically improving designs base…