18 citations · 42 across the 21 of their papers we have counts for
10 papers · 2 filters
YuLan-Mini: An Open Data-efficient Language Model
Yiwen Hu, Huatong Song, Jia Deng +8
Effective pre-training of large language models (LLMs) has been challenging due to the immense resource demands and the complexity of the technical processes involved. This paper p…
Extracting and Combining Abilities For Building Multi-lingual Ability-enhanced Large Language Models
Zhipeng Chen, Kun Zhou, Liang Song +4
Multi-lingual ability transfer has become increasingly important for the broad application of large language models (LLMs). Existing work highly relies on training with the multi-l…
Towards Effective and Efficient Continual Pre-training of Large Language Models
Jie Chen, Zhipeng Chen, Jiapeng Wang +16
Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. To make the CPT approach more traceable, this paper presents…
LLMBox: A Comprehensive Library for Large Language Models
Tianyi Tang, Yiwen Hu, Bingqian Li +22
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. T…
YuLan: An Open-source Large Language Model
Yutao Zhu, Kun Zhou, Kelong Mao +35
Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many o…
Not Everything is All You Need: Toward Low-Redundant Optimization for Large Language Model Alignment
Zhipeng Chen, Kun Zhou, Wayne Xin Zhao +2
Large language models (LLMs) are still struggling in aligning with human preference in complex tasks and scenarios. They are prone to overfit into the unexpected patterns or superf…
JiuZhang3.0: Efficiently Improving Mathematical Reasoning by Training Small Data Synthesis Models
Kun Zhou, Beichen Zhang, Jiapeng Wang +6
Mathematical reasoning is an important capability of large language models~(LLMs) for real-world applications. To enhance this capability, existing work either collects large-scale…
KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph
Jinhao Jiang, Kun Zhou, Wayne Xin Zhao +4
In this paper, we aim to improve the reasoning ability of large language models (LLMs) over knowledge graphs (KGs) to answer complex questions. Inspired by existing methods that de…
Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning
Yingqian Min, Kun Zhou, Dawei Gao +3
Recently, multi-task instruction tuning has been applied into sentence representation learning, which endows the capability of generating specific representations with the guidance…
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint
Zhipeng Chen, Kun Zhou, Wayne Xin Zhao +4
Reinforcement learning (RL) has been widely used in training large language models (LLMs) for preventing unexpected outputs, eg reducing harmfulness and errors. However, existing R…