4 papers
NanoNet: Parameter-Efficient Learning with Label-Scarce Supervision for Lightweight Text Mining Model
Qianren Mao, Yashuo Luo, Ziqi Qin +12
The lightweight semi-supervised learning (LSL) strategy provides an effective approach of conserving labeled samples and minimizing model inference costs. Prior research has effect…
Building a Human-Verified Clinical Reasoning Dataset via a Human LLM Hybrid Pipeline for Trustworthy Medical AI
Chao Ding, Mouxiao Bian, Pengcheng Chen +11
Despite strong performance in medical question-answering, the clinical adoption of Large Language Models (LLMs) is critically hampered by their opaque 'black-box' reasoning, limiti…
Harnessing Multiple Large Language Models: A Survey on LLM Ensemble
Zhijun Chen, Xiaodong Lu, Jingzheng Li +12
LLM Ensemble -- which involves the comprehensive use of multiple large language models (LLMs), each aimed at handling user queries during downstream inference, to benefit from thei…
Exploiting Latent Linearity in LLMs Improves Explainable Molecular Representation Learning
Zhuoran Li, Xu Sun, Wanyu Lin +1
Large language models (LLMs) have demonstrated broad utility across molecular domains, spanning drug discovery and materials design. Analyzing LLMs' latent representations is cruci…