87 citations · 112 across the 8 of their papers we have counts for
8 papers
RankEvolve: Automating the Discovery of Retrieval Algorithms via LLM-Driven Evolution
Jinming Nian, Fangchen Li, Dae Hoon Park +1
Retrieval algorithms like BM25 and query likelihood with Dirichlet smoothing remain strong and efficient first-stage rankers, yet improvements have mostly relied on parameter tunin…
Submodular Evaluation Subset Selection in Automatic Prompt Optimization
Jinming Nian, Zhiyuan Peng, Hongwei Shang +2
Automatic prompt optimization reduces manual prompt engineering, but relies on task performance measured on a small, often randomly sampled evaluation subset as its main source of…
DSAI: Unbiased and Interpretable Latent Feature Extraction for Data-Centric AI
Hyowon Cho, Soonwon Ka, Daechul Park +3
Large language models (LLMs) often struggle to objectively identify latent characteristics in large datasets due to their reliance on pre-trained knowledge rather than actual data…
Compiler-Level Matrix Multiplication Optimization for Deep Learning
Huaqing Zhang, Xiaolin Cheng, Hui Zang +1
An important linear algebra routine, GEneral Matrix Multiplication (GEMM), is a fundamental operator in deep learning. Compilers need to translate these routines into low-level cod…
Gradient-Coherent Strong Regularization for Deep Neural Networks
Dae Hoon Park, Chiu Man Ho, Yi Chang +1
Regularization plays an important role in generalization of deep neural networks, which are often prone to overfitting with their numerous parameters. L1 and L2 regularizers are co…
Adversarial Sampling and Training for Semi-Supervised Information Retrieval
Dae Hoon Park, Yi Chang
Ad-hoc retrieval models with implicit feedback often have problems, e.g., the imbalanced classes in the data set. Too few clicked documents may hurt generalization ability of the m…