AIConfPaper
← Search

Michelle Yuan

5 accepted papers

2025

ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly

Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek +2
COLING 2025main

Information extraction (IE) needs vary over time, where a flexible information extraction (IE) system can be useful. Despite this, existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user’s n…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

Automated Composition of Agents: A Knapsack Approach for Agentic Component Selection

Michelle Yuan, Khushbu Pahwa, Shuaichen Chang, Mustafa Devrim Kaba, MONICA SUNKARA, Jiarong Jiang +2
NeurIPS 2025poster

Designing effective agentic systems requires the seamless composition and integration of agents, tools, and models within dynamic and uncertain environments. Most existing methods rely on static, semantic retrieval approaches for tool or agent discovery. However, effective reuse and composition of e…

Cited by 0Source ↗Scholar ↗
2025

MemInsight: Autonomous Memory Augmentation for LLM Agents

Rana Salama, Jason Cai, Michelle Yuan, Anna Currey, Monica Sunkara, Yi Zhang +1
EMNLP 2025

Large language model (LLM) agents have evolved to intelligently process information, make decisions, and interact with users or tools. A key capability is the integration of long-term memory capabilities, enabling these agents to draw upon historical interactions and knowledge. However, the growing

Cited by 0Source ↗PDF ↗Scholar ↗
2022

Adapting Coreference Resolution Models through Active Learning

Michelle Yuan, Patrick Xia, Chandler May, Benjamin Van Durme, Jordan Boyd-Graber
ACL 2022long

Neural coreference resolution models trained on one dataset may not transfer to new, low-resource domains. Active learning mitigates this problem by sampling a small subset of data for annotators to label. While active learning is well-defined for classification tasks, its application to coreference…

Cited by 32Source ↗PDF ↗code ↗Scholar ↗
2018

Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages

Michelle Yuan, Benjamin Van Durme, Jordan L Ying
NeurIPS 2018poster

Multilingual topic models can reveal patterns in cross-lingual document collections. However, existing models lack speed and interactivity, which prevents adoption in everyday corpora exploration or quick moving situations (e.g., natural disasters, political instability). First, we propose a multili…

Cited by 47Source ↗PDF ↗code ↗Scholar ↗