5 citations · 6 across the 7 of their papers we have counts for
7 papers
Beyond Length: Quantifying Long-Range Information for Long-Context LLM Pretraining Data
Haoran Deng, Yingyu Lin, Zhenghao Lin +4
Long-context language models unlock advanced capabilities in reasoning, code generation, and document summarization by leveraging dependencies across extended spans of text. Howeve…
Lossless Token Sequence Compression via Meta-Tokens
John Harvill, Ziwei Fan, Hao Wang +4
Existing work on prompt compression for Large Language Models (LLM) focuses on lossy methods that try to maximize the retention of semantic information that is relevant to downstre…
Agree to Disagree? A Meta-Evaluation of LLM Misgendering
Arjun Subramonian, Vagrant Gautam, Preethi Seshadri +3
Numerous methods have been proposed to measure LLM misgendering, including probability-based evaluations (e.g., automatically with templatic sentences) and generation-based evaluat…
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks
Arjun Subramonian, Jian Kang, Yizhou Sun
Graph Neural Networks (GNNs) often perform better for high-degree nodes than low-degree nodes on node classification tasks. This degree bias can reinforce social marginalization by…
Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction
Arjun Subramonian, Levent Sagun, Yizhou Sun
Graph neural network (GNN) link prediction is increasingly deployed in citation, collaboration, and online social networks to recommend academic literature, collaborators, and frie…
Question-Answer Sentence Graph for Joint Modeling Answer Selection
Roshni G. Iyer, Thuy Vu, Alessandro Moschitti +1
This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems. During offline learnin…
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics
Chunheng Jiang, Tejaswini Pedapati, Pin-Yu Chen +2
Efficient model selection for identifying a suitable pre-trained neural network to a downstream task is a fundamental yet challenging task in deep learning. Current practice requir…