733 citations · 2.4k across the 107 of their papers we have counts for
6 papers · 2 filters
Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue
Junkai Zhou, Liang Pang, Huawei Shen +1
The emergence of large language models (LLMs) further improves the capabilities of open-domain dialogue systems and can generate fluent, coherent, and diverse responses. However, L…
RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling
Jingcheng Deng, Liang Pang, Huawei Shen +1
Retrieval-augmented language models show promise in addressing issues like outdated information and hallucinations in language models (LMs). However, current research faces two mai…
LLMDet: A Third Party Large Language Models Generated Text Detection Tool
Kangxi Wu, Liang Pang, Huawei Shen +2
Generated texts from large language models (LLMs) are remarkably close to high-quality human-authored text, raising concerns about their potential misuse in spreading false informa…
MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space
Hanxing Ding, Liang Pang, Zihao Wei +3
Multi-aspect controllable text generation aims to generate fluent sentences that possess multiple desired attributes simultaneously. Traditional methods either combine many operato…
Search-in-the-Chain: Interactively Enhancing Large Language Models with Search for Knowledge-intensive Tasks
Shicheng Xu, Liang Pang, Huawei Shen +2
Making the content generated by Large Language Model (LLM), accurate, credible and traceable is crucial, especially in complex knowledge-intensive tasks that require multi-step rea…
Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding
Yunchang Zhu, Liang Pang, Kangxi Wu +3
Current natural language understanding (NLU) models have been continuously scaling up, both in terms of model size and input context, introducing more hidden and input neurons. Whi…