2.3k citations · 2.5k across the 38 of their papers we have counts for
7 papers · 1 filter
PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR
James Burgess, Jan N. Hansen, Duo Peng +5
Search agents are language models (LMs) that reason and search knowledge bases (or the web) to answer questions; recent methods supervise only the final answer accuracy using reinf…
Three Forms of Stochastic Injection for Improved Distribution-to-Distribution Generative Modeling
Shiye Su, Yuhui Zhang, Linqi Zhou +2
Modeling transformations between arbitrary data distributions is a fundamental scientific challenge, arising in applications like drug discovery and evolutionary simulation. While…
EquiBench: Benchmarking Large Language Models' Reasoning about Program Semantics via Equivalence Checking
Anjiang Wei, Jiannan Cao, Ran Li +8
As large language models (LLMs) become integral to code-related tasks, a central question emerges: Do LLMs truly understand program semantics? We introduce EquiBench, a new benchma…
Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data
Yuhui Zhang, Elaine Sui, Serena Yeung-Levy
Building cross-modal applications is challenging due to limited paired multi-modal data. Recent works have shown that leveraging a pre-trained multi-modal contrastive representatio…
Can large language models provide useful feedback on research papers? A large-scale empirical analysis
Weixin Liang, Yuhui Zhang, Hancheng Cao +9
Expert feedback lays the foundation of rigorous research. However, the rapid growth of scholarly production and intricate knowledge specialization challenge the conventional scient…
Diagnosing and Rectifying Vision Models using Language
Yuhui Zhang, Jeff Z. HaoChen, Shih-Cheng Huang +3
Recent multi-modal contrastive learning models have demonstrated the ability to learn an embedding space suitable for building strong vision classifiers, by leveraging the rich inf…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…