29 citations · 195 across the 32 of their papers we have counts for
5 papers · 1 filter
To Err Is Human: Systematic Quantification of Errors in Published AI Papers via LLM Analysis
Federico Bianchi, Yongchan Kwon, Zachary Izzo +2
How many mistakes do published AI papers contain? Peer-reviewed publications form the foundation upon which new research and knowledge are built. Errors that persist in the literat…
Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails
Siwei Han, Kaiwen Xiong, Jiaqi Liu +9
As Large Language Model (LLM) agents increasingly gain self-evolutionary capabilities to adapt and refine their strategies through real-world interaction, their long-term reliabili…
UQ: Assessing Language Models on Unsolved Questions
Fan Nie, Ken Ziyu Liu, Zihao Wang +11
Benchmarks shape progress in AI research. A useful benchmark should be both difficult and realistic: questions should challenge frontier models while also reflecting real-world usa…
Optimas: Optimizing Compound AI Systems with Globally Aligned Local Rewards
Shirley Wu, Parth Sarthi, Shiyu Zhao +10
Compound AI systems integrating multiple components, such as Large Language Models, specialized tools, and traditional machine learning models, are increasingly deployed to solve c…
An Overview of Large Language Models for Statisticians
Wenlong Ji, Weizhe Yuan, Emily Getzen +7
Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI), exhibiting remarkable capabilities across diverse tasks such as text generation,…