Bill Lin

FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks

2021 · arXiv (Cornell University)

Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP) tasks. Federated learning (FL) provides promising approaches for a large number of clients …

On the Robustness of Reading Comprehension Models to Entity Renaming

2021 · arXiv (Cornell University)

We study the robustness of machine reading comprehension (MRC) models to entity renaming -- do models make more wrong predictions when the same questions are asked about an entity whose name has been changed? Such …

Tabular machine learning using conjunctive threshold neural networks

2022 · Machine Learning with Applications

We propose a novel three-layer neural network architecture with threshold activations for tabular data classification problems. The hidden layer units correspond to trainable neurons with arbitrary weights and biases and a step activation. These neurons …

LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

2023

We present LLM-Blender, an ensembling framework designed to attain consistently superior performance by leveraging the diverse strengths of multiple open-source large language models (LLMs). Our framework consists of two modules: PairRanker and GenFuser, addressing the …

Monolithic Silicon‐Photonics Sign‐Sign Dot‐Product Circuits Toward Artificial Intelligence and Multiple‐Input Multiple‐Output Accelerators

2026 · Nanophotonics

ABSTRACT This research leverages advanced monolithic silicon‐photonics integrated‐circuit manufacturing capabilities to realize system‐on‐chip photonic‐computing‐based linear‐algebra accelerators for a wide range of applications in artificial intelligence, machine learning, and multiple‐input multiple‐output wireless technology. With holistic codesign …