activity
20212026
most citedCPT: A Pre-Trained Unbalanced Transformer for Both Chinese Language Understanding and Generation

110 citations · 159 across the 11 of their papers we have counts for

collaborators

11 papers

cs.SD2026

OmniVAE: An Audio-Video VAE with Cross-Modal Alignment for Joint Generation

Jun Zhan, Chen Yang, Yitian Gong +23

Recent generative models are moving beyond silent video or standalone audio synthesis toward the joint generation of synchronized audio and video. Despite this progress, jointly ge…

cs.CL2025

Zero-RAG: Towards Retrieval-Augmented Generation with Zero Redundant Knowledge

Qi Luo, Xiaonan Li, Junqi Dai +2

Retrieval-Augmented Generation has shown remarkable results to address Large Language Models' hallucinations, which usually uses a large external corpus to supplement knowledge to…

cs.CL2024★ 3 cited

AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling

Jun Zhan, Junqi Dai, Jiasheng Ye +13

We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images…

cs.CL2023

Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication

Zhangyue Yin, Qiushi Sun, Cheng Chang +4

Large Language Models (LLMs) have recently made significant strides in complex reasoning tasks through the Chain-of-Thought technique. Despite this progress, their reasoning is oft…

cs.CL2023★ 4 cited

Character-LLM: A Trainable Agent for Role-Playing

Yunfan Shao, Linyang Li, Junqi Dai +1

Large language models (LLMs) can be used to serve as agents to simulate human behaviors, given the powerful ability to understand human instructions and provide high-quality genera…

cs.CL2022

Dialogue Meaning Representation for Task-Oriented Dialogue Systems

Xiangkun Hu, Junqi Dai, Hang Yan +4

Dialogue meaning representation formulates natural language utterance semantics in their conversational context in an explicit and machine-readable form. Previous work typically fo…