2 citations · 4 across the 7 of their papers we have counts for
11 papers · 1 filter
PrimeScientist: Strategic Allocation of Research Effort in Autonomous Research
Xinle Yu, Fan Bai, Kaiser Sun +5
Autonomous research agents aim to automate scientific workflows, from proposing ideas to conducting experiments and analyzing results. Yet current AI and research agents can propos…
Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States
Zixuan Wang, Yufan Zhou, Jinzhou Tang +16
As language models become more capable, long-term collaboration in learning, reasoning, and decision-making calls for a deeper understanding of the people they serve. Yet training…
Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs
Kaiser Sun, Xiaochuang Yuan, Hongjun Liu +4
Multimodal large language models (MLLMs) can process text presented as images, yet they often perform worse than when the same content is provided as textual tokens. We systematica…
Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict
Kaiser Sun, Fan Bai, Mark Dredze
Large language models (LLMs) draw on both contextual information and parametric memory, yet these sources can conflict. Prior studies have largely examined this issue in contextual…
LLMs are Better Than You Think: Label-Guided In-Context Learning for Named Entity Recognition
Fan Bai, Hamid Hassanzadeh, Ardavan Saeedi +1
In-context learning (ICL) enables large language models (LLMs) to perform new tasks using only a few demonstrations. However, in Named Entity Recognition (NER), existing ICL method…
Give me Some Hard Questions: Synthetic Data Generation for Clinical QA
Fan Bai, Keith Harrigian, Joel Stremmel +3
Clinical Question Answering (QA) systems enable doctors to quickly access patient information from electronic health records (EHRs). However, training these systems requires signif…