21 citations · 28 across the 22 of their papers we have counts for
5 papers · 1 filter
Towards Greater Leverage: Scaling Laws for Efficient Mixture-of-Experts Language Models
Changxin Tian, Kunlong Chen, Jia Liu +3
Mixture-of-Experts (MoE) has become a dominant architecture for scaling Large Language Models (LLMs) efficiently by decoupling total parameters from computational cost. However, th…
Enhancing LLM Tool Use with High-quality Instruction Data from Knowledge Graph
Jingwei Wang, Zai Zhang, Hao Qian +7
Teaching large language models (LLMs) to use tools is crucial for improving their problem-solving abilities and expanding their applications. However, effectively using tools is ch…
LookAhead Tuning: Safer Language Models via Partial Answer Previews
Kangwei Liu, Mengru Wang, Yujie Luo +7
Fine-tuning enables large language models (LLMs) to adapt to specific domains, but often compromises their previously established safety alignment. To mitigate the degradation of m…
MAQInstruct: Instruction-based Unified Event Relation Extraction
Jun Xu, Mengshu Sun, Zhiqiang Zhang +1
Extracting event relations that deviate from known schemas has proven challenging for previous methods based on multi-class classification, MASK prediction, or prototype matching.…
Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis
Lin Yuan, Jun Xu, Honghao Gui +4
High-quality, large-scale instructions are crucial for aligning large language models (LLMs), however, there is a severe shortage of instruction in the field of natural language un…