1 citations · 1 across the 12 of their papers we have counts for
12 papers
DataEvolver: Automatic Data Preparation for Large Language Models through Multi-Level Self-Evolving
Chao Deng, Shaolei Zhang, Ju Fan +1
High-quality training data is essential to large language models (LLMs) and typically requires extensive and costly manual curation. Automatic data preparation systems support reus…
JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data
Junlan Feng, Fanyu Meng, Chong Long +12
We introduce JT-Safe-V2, a large language model designed to advance the safety and trustworthiness of foundation models, extending our previous JT-Safe model toward a more comprehe…
Self-Correction Distillation for Structured Data Question Answering
Yushan Zhu, Wen Zhang, Long Jin +8
Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have…
JT-Safe: Intrinsically Enhancing the Safety and Trustworthiness of LLMs
Junlan Feng, Fanyu Meng, Chong Long +12
The hallucination and credibility concerns of large language models (LLMs) are global challenges that the industry is collectively addressing. Recently, a significant amount of adv…
Towards Generalized Routing: Model and Agent Orchestration for Adaptive and Efficient Inference
Xiyu Guo, Shan Wang, Chunfang Ji +6
The rapid advancement of large language models (LLMs) and domain-specific AI agents has greatly expanded the ecosystem of AI-powered services. User queries, however, are highly div…
JT-Math: A Multi-Stage Framework for Advanced Mathematical Reasoning in Large Language Models
Yifan Hao, Fangning Chao, Yaqian Hao +6
Mathematical reasoning is a cornerstone of artificial general intelligence and a primary benchmark for evaluating the capabilities of Large Language Models (LLMs). While state-of-t…
TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models
Ce Li, Xiaofan Liu, Zhiyan Song +9
The majority of data in businesses and industries is stored in tables, databases, and data warehouses. Reasoning with table-structured data poses significant challenges for large l…
GenDistiller: Distilling Pre-trained Language Models based on an Autoregressive Generative Model
Yingying Gao, Shilei Zhang, Chao Deng +1
Pre-trained speech language models such as HuBERT and WavLM leverage unlabeled speech data for self-supervised learning and offer powerful representations for numerous downstream t…
PolySpeech: Exploring Unified Multitask Speech Models for Competitiveness with Single-task Models
Runyan Yang, Huibao Yang, Xiqing Zhang +6
Recently, there have been attempts to integrate various speech processing tasks into a unified model. However, few previous works directly demonstrated that joint optimization of d…
Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural Network
Yanan Chen, Zihao Cui, Yingying Gao +3
The expectation to deploy a universal neural network for speech enhancement, with the aim of improving noise robustness across diverse speech processing tasks, faces challenges due…
Depth Map Denoising Network and Lightweight Fusion Network for Enhanced 3D Face Recognition
Ruizhuo Xu, Ke Wang, Chao Deng +5
With the increasing availability of consumer depth sensors, 3D face recognition (FR) has attracted more and more attention. However, the data acquired by these sensors are often co…
GenDistiller: Distilling Pre-trained Language Models based on Generative Models
Yingying Gao, Shilei Zhang, Zihao Cui +3
Self-supervised pre-trained models such as HuBERT and WavLM leverage unlabeled speech data for representation learning and offer significantly improve for numerous downstream tasks…