activity
20222026
most citedDeveloping ChemDFM as a large language foundation model for chemistry

36 citations · 42 across the 14 of their papers we have counts for

collaborators
Showing 2024Show all

7 papers · 1 filter

cs.CV2024

VQTalker: Towards Multilingual Talking Avatars through Facial Motion Tokenization

Tao Liu, Ziyang Ma, Qi Chen +4

We present VQTalker, a Vector Quantization-based framework for multilingual talking head generation that addresses the challenges of lip synchronization and natural motion across d…

cs.CL2024

Sparsity-Accelerated Training for Large Language Models

Da Ma, Lu Chen, Pengyu Wang +6

Large language models (LLMs) have demonstrated proficiency across various natural language processing (NLP) tasks but often require additional training, such as continual pre-train…

eess.AS2024★ 2 cited

GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement

Yifan Yang, Zheshu Song, Jianheng Zhuo +13

The evolution of speech technology has been spurred by the rapid increase in dataset sizes. Traditional speech models generally depend on a large amount of labeled training data, w…

cs.CV2024

AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding

Tao Liu, Feilong Chen, Shuai Fan +4

The paper introduces AniTalker, an innovative framework designed to generate lifelike talking faces from a single portrait. Unlike existing models that primarily focus on verbal cu…

eess.AS2024

The X-LANCE Technical Report for Interspeech 2024 Speech Processing Using Discrete Speech Unit Challenge

Yiwei Guo, Chenrun Wang, Yifan Yang +9

Discrete speech tokens have been more and more popular in multiple speech processing fields, including automatic speech recognition (ASR), text-to-speech (TTS) and singing voice sy…

cs.CL2024★ 2 cited

Rejection Improves Reliability: Training LLMs to Refuse Unknown Questions Using RL from Knowledge Feedback

Hongshen Xu, Zichen Zhu, Situo Zhang +4

Large Language Models (LLMs) often generate erroneous outputs, known as hallucinations, due to their limitations in discerning questions beyond their knowledge scope. While address…

cs.CL2024★ 36 cited

Developing ChemDFM as a large language foundation model for chemistry

Zihan Zhao, Da Ma, Lu Chen +11

Artificial intelligence (AI) has played an increasingly important role in chemical research. However, most models currently used in chemistry are specialist models that require tra…