Large Language Model
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution Haoran Y e
The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design. The long-standing endeavor of design automation has gained new momentum with the rise of large language models (LLMs).
Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion Filip Szatkowski IDEAS NCBR Warsaw University of Technology Bartosz Wójcik
Finally, we develop an efficient implementation that translates these computational savings into actual wall-clock speedup.
Learning Formal Mathematics From Intrinsic Motivation Gabriel Poesia 1 David Broman 4 Nick Haber 1,3 Noah D. Goodman
How did humanity coax mathematics from the æther?
Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning Yiming Wang α Pei Zhang β, γ Baosong Y ang
Real-world data deviating from the independent and identically distributed ( i.i.d.)
HEMM: Holistic Evaluation of Multimodal Foundation Models Paul Pu Liang
To address this need, we contribute Holistic Evaluation of Multimodal Models ( HEMM), visualized in Figure 1. HEMM, as an evaluation framework, goes beyond conventional lists of datasets to emphasize holistic benchmarking at three levels.
Slicing Vision Transformer for Flexible Inference
Vision Transformers (ViT) is known for its scalability.