2 citations · 8 across the 15 of their papers we have counts for
6 papers · 1 filter
End-to-End On-Device Quantization-Aware Training for LLMs at Inference Cost
Qitao Tan, Xiaoying Song, Jin Lu +9
Quantization is an effective technique to reduce the deployment cost of large language models (LLMs), and post-training quantization (PTQ) has been widely studied due to its effici…
Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL
Xiaoying Song, Anirban Saha Anik, Dibakar Barua +3
Health misinformation spreading online poses a significant threat to public health. Researchers have explored methods for automatically generating counterspeech to health misinform…
A Dynamic Fusion Model for Consistent Crisis Response
Xiaoying Song, Anirban Saha Anik, Eduardo Blanco +2
In response to the urgent need for effective communication with crisis-affected populations, automated responses driven by language models have been proposed to assist in crisis co…
Multi-Agent Retrieval-Augmented Framework for Evidence-Based Counterspeech Against Health Misinformation
Anirban Saha Anik, Xiaoying Song, Elliott Wang +3
Large language models (LLMs) incorporated with Retrieval-Augmented Generation (RAG) have demonstrated powerful capabilities in generating counterspeech against misinformation. Howe…
A Hybrid Framework for Subject Analysis: Integrating Embedding-Based Regression Models with Large Language Models
Jinyu Liu, Xiaoying Song, Diana Zhang +3
Providing subject access to information resources is an essential function of any library management system. Large language models (LLMs) have been widely used in classification an…
Echoes of Discord: Forecasting Hater Reactions to Counterspeech
Xiaoying Song, Sharon Lisseth Perez, Xinchen Yu +2
Hate speech (HS) erodes the inclusiveness of online users and propagates negativity and division. Counterspeech has been recognized as a way to mitigate the harmful consequences. W…