3 papers
cs.CL2026
Towards Principled Design of Mixture-of-Experts Language Models under Memory and Inference Constraints
Seng Pei Liew, Kenta Shinzato, Yuyang Dong
Modern Mixture-of-Experts (MoE) language models are designed based on total parameters (memory footprint) and active parameters (inference cost). However, we find these two factors…
cs.CL2023
PHALM: Building a Knowledge Graph from Scratch by Prompting Humans and a Language Model
Tatsuya Ide, Eiki Murata, Daisuke Kawahara +4
Despite the remarkable progress in natural language understanding with pretrained Transformers, neural language models often do not handle commonsense knowledge well. Toward common…
cs.CL2022
Building a Personalized Dialogue System with Prompt-Tuning
Tomohito Kasahara, Daisuke Kawahara, Nguyen Tung +3
Dialogue systems without consistent responses are not fascinating. In this study, we build a dialogue system that can respond based on a given character setting (persona) to bring…