ELAD: Explanation-Guided Large Language Models Active Distillation

no code implementations • 20 Feb 2024 • Yifei Zhang, Bo Pan, Chen Ling, Yuntong Hu, Liang Zhao

The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences.

Active Learning Knowledge Distillation

Distilling Large Language Models for Text-Attributed Graph Learning

no code implementations • 19 Feb 2024 • Bo Pan, Zheng Zhang, Yifei Zhang, Yuntong Hu, Liang Zhao

To address the inherent gaps between LLMs (generative models for texts) and graph models (discriminative models for graphs), we propose first to let LLMs teach an interpreter with rich textual rationale and then let a student model mimic the interpreter's reasoning without LLMs' textual rationale.

Graph Learning TAG

Beyond Text: A Deep Dive into Large Language Models' Ability on Understanding Graph Data

no code implementations • 7 Oct 2023 • Yuntong Hu, Zheng Zhang, Liang Zhao

Large language models (LLMs) have achieved impressive performance on many natural language processing tasks.

Benchmarking