6 citations · 8 across the 11 of their papers we have counts for
11 papers
Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain
Yulin Luo, Chun-Kai Fan, Menghang Dong +19
Building robots that can perceive, reason, and act in dynamic, unstructured environments remains a central challenge. Recent embodied systems often follow a dual-system paradigm, w…
RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL System
Zui Chen, Han Li, Xinhao Zhang +12
We present RubikSQL, a novel NL2SQL system designed to address key challenges in real-world enterprise-level NL2SQL, such as implicit intents and domain-specific terminology. Rubik…
Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models
Jijie Li, Li Du, Hanyu Zhao +5
Large Language Models (LLMs) demonstrate strong performance in real-world applications, yet existing open-source instruction datasets often concentrate on narrow domains, such as m…
InCo-DPO: Balancing Distribution Shift and Data Quality for Enhanced Preference Optimization
Yunan Wang, Jijie Li, Bo-Wen Zhang +2
Direct Preference Optimization (DPO) optimizes language models to align with human preferences. Utilizing on-policy samples, generated directly by the policy model, typically resul…
Predictable Emergent Abilities of LLMs: Proxy Tasks Are All You Need
Bo-Wen Zhang, Yan Yan, Boxiang Yang +2
While scaling laws optimize training configurations for large language models (LLMs) through experiments on smaller or early-stage models, they fail to predict emergent abilities d…
CCI3.0-HQ: a large-scale Chinese dataset of high quality designed for pre-training large language models
Liangdong Wang, Bo-Wen Zhang, Chengwei Wu +7
We present CCI3.0-HQ (https://huggingface.co/datasets/BAAI/CCI3-HQ), a high-quality 500GB subset of the Chinese Corpora Internet 3.0 (CCI3.0)(https://huggingface.co/datasets/BAAI/C…
Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
Shuhao Gu, Jialing Zhang, Siyuan Zhou +23
Recently, Vision-Language Models (VLMs) have achieved remarkable progress in multimodal tasks, and multimodal instruction data serves as the foundation for enhancing VLM capabiliti…
Aquila2 Technical Report
Bo-Wen Zhang, Liangdong Wang, Jijie Li +6
This paper introduces the Aquila2 series, which comprises a wide range of bilingual models with parameter sizes of 7, 34, and 70 billion. These models are trained based on an innov…
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies
Bo-Wen Zhang, Liangdong Wang, Ye Yuan +24
In recent years, with the rapid application of large language models across various fields, the scale of these models has gradually increased, and the resources required for their…
InfinityMATH: A Scalable Instruction Tuning Dataset in Programmatic Mathematical Reasoning
Bo-Wen Zhang, Yan Yan, Lin Li +1
Recent advancements in Chain-of-Thoughts (CoT) and Program-of-Thoughts (PoT) methods have greatly enhanced language models' mathematical reasoning capabilities, facilitating their…
TACO: Topics in Algorithmic COde generation dataset
Rongao Li, Jie Fu, Bo-Wen Zhang +6
We introduce TACO, an open-source, large-scale code generation dataset, with a focus on the optics of algorithms, designed to provide a more challenging training dataset and evalua…