7 citations · 22 across the 15 of their papers we have counts for
8 papers · 1 filter
Verb-ICL: Rethinking In-Context Learning for Structured Prediction
Fan Bai, Hengshuo Miao, Sanjit S Batra +3
Structured prediction tasks pose unique challenges for in-context learning (ICL): their compositional outputs require modeling fine-grained, token-level patterns that sentence-leve…
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…
Not Another Text Benchmark: Putting the "Visual" Back in Visual Question Answering for Large Video Models
Rwiddhi Chakraborty, Yinong, Yinong Oliver Wang +9
Large video models have exhibited impressive performance on a wide range of visual question answering tasks, owing to the rise of powerful, pretrained text and vision encoders. The…
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…
Demystifying Training-Time Augmentation for Data-Constrained Language Model Pretraining
Michael K. Chen, Xikun Zhang, Fan Bai +2
As AI labs approach a data ceiling where compute capacity outpaces the rate of new high-quality text generation, language model pretraining is shifting toward a data-constrained, c…
Agent-as-Peer-Debriefer: A Multi-Agent Framework with Perspective-Based Refinement for Qualitative Analysis
Zhimin Lin, Kun Cheng, Zhiyao Shu +4
Large language models (LLMs) are increasingly used for qualitative data analysis (QDA), yet their outputs often miss the depth and nuance of human analysis. We argue this gap refle…
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…
FIRE-Bench: Evaluating AI Agents on the Rediscovery of Scientific Insights
Zhen Wang, Fan Bai, Zhongyan Luo +9
Autonomous agents powered by large language models (LLMs) promise to accelerate scientific discovery end-to-end, but rigorously evaluating their capacity for verifiable discovery r…