4 papers
Learning Personalized Agents from Human Feedback
Kaiqu Liang, Julia Kruk, Shengyi Qian +9
Modern AI agents are powerful but often fail to align with the idiosyncratic, evolving preferences of individual users. Prior approaches typically rely on static datasets, either t…
Your thoughts tell who you are: Characterize the reasoning patterns of LRMs
Yida Chen, Yuning Mao, Xianjun Yang +7
Current comparisons of large reasoning models (LRMs) focus on macro-level statistics such as task accuracy or reasoning length. Whether different LRMs reason differently remains an…
High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning
Tim Franzmeyer, Archie Sravankumar, Lijuan Liu +6
Large Language Models (LLMs) currently respond to every prompt. However, they can produce incorrect answers when they lack knowledge or capability -- a problem known as hallucinati…
Diversity-driven Data Selection for Language Model Tuning through Sparse Autoencoder
Xianjun Yang, Shaoliang Nie, Lijuan Liu +5
Instruction tuning data are often quantity-saturated due to the large volume of data collection and fast model iteration, leaving data selection important but underexplored. Existi…