Shunan Guo — accepted AI-conference papers

2024

RaDA: Retrieval-augmented Web Agent Planning with LLMs

Minsoo Kim, Victor Bursztyn, Eunyee Koh, Shunan Guo, Seung-won Hwang

ACL 2024findings

Agents powered by large language models (LLMs) inherit important limitations, such as the restricted context length, dependency on human-engineered exemplars (e.g., for task decomposition), and insufficient generalization. To address these challenges, we propose RaDA, a novel planning method for Web…

Cited by 6 Source ↗PDF ↗Scholar ↗

2023

Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs

Sudhanshu Chanpuriya, Ryan A. Rossi, Sungchul Kim, Tong Yu, Jane Hoffswell, Nedim Lipka +2

ICLR 2023poster

Temporal networks model a variety of important phenomena involving timed interactions between entities. Existing methods for machine learning on temporal networks generally exhibit at least one of two limitations. First, many methods assume time to be discretized, so if the time data is continuous,…

Cited by 9 Source ↗PDF ↗Scholar ↗