4 papers
Understanding Axes of Difficulty For Long Context Tasks Via PredicateLongBench
Siddhartha Jain, Ameya Velingker
Large language models (LLMs) have demonstrated rapidly improving long-context capabilities, prompting a wave of benchmarks designed to evaluate them. However, existing long-context…
Linear Space Streaming Lower Bounds for Approximating CSPs
Chi-Ning Chou, Alexander Golovnev, Madhu Sudan +2
We consider the approximability of constraint satisfaction problems in the streaming setting. For every constraint satisfaction problem (CSP) on variables taking values in $\{0…
Even Sparser Graph Transformers
Hamed Shirzad, Honghao Lin, Balaji Venkatachalam +3
Graph Transformers excel in long-range dependency modeling, but generally require quadratic memory complexity in the number of nodes in an input graph, and hence have trouble scali…
A Theory for Compressibility of Graph Transformers for Transductive Learning
Hamed Shirzad, Honghao Lin, Ameya Velingker +3
Transductive tasks on graphs differ fundamentally from typical supervised machine learning tasks, as the independent and identically distributed (i.i.d.) assumption does not hold a…