4 citations · 4 across the 5 of their papers we have counts for
5 papers
GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Models
Oscar Rivera, Ziqing Wang, Matthieu Dagommer +2
Machine learning accelerates molecular property prediction, yet state-of-the-art Large Language Models and Graph Neural Networks operate as black boxes. In drug discovery, where sa…
RAPTOR: Ridge-Adaptive Logistic Probes
Ziqi Gao, Yaotian Zhu, Qingcheng Zeng +4
Probing studies what information is encoded in a frozen LLM's layer representations by training a lightweight predictor on top of them. Beyond analysis, probes are often used opera…
Fact or Facsimile? Evaluating the Factual Robustness of Modern Retrievers
Haoyu Wu, Qingcheng Zeng, Kaize Ding
Dense retrievers and rerankers are central to retrieval-augmented generation (RAG) pipelines, where accurately retrieving factual information is crucial for maintaining system trus…
Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models
Qingcheng Zeng, Mingyu Jin, Qinkai Yu +12
Large Language Models (LLMs) are employed across various high-stakes domains, where the reliability of their outputs is crucial. One commonly used method to assess the reliability…
Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?
Mingyu Jin, Qinkai Yu, Jingyuan Huang +10
Large language models (LLMs) have shown remarkable performances across a wide range of tasks. However, the mechanisms by which these models encode tasks of varying complexities rem…