110 citations · 228 across the 17 of their papers we have counts for
21 papers · 1 filter
Qwen Technical Report
Jinze Bai, Shuai Bai, Yunfei Chu +45
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to hu…
Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
Zheng Yuan, Hongyi Yuan, Chengpeng Li +5
Mathematical reasoning is a challenging task for large language models (LLMs), while the scaling relationship of it with respect to LLM capacity is under-explored. In this paper, w…
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning
Jianing Wang, Chengyu Wang, Chuanqi Tan +2
Large language models (LLMs) enable in-context learning (ICL) by conditioning on a few labeled training examples as a text-based prompt, eliminating the need for parameter updates…
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models
Keming Lu, Hongyi Yuan, Zheng Yuan +5
Foundation language models obtain the instruction-following ability through supervised fine-tuning (SFT). Diversity and complexity are considered critical factors of a successful S…
Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning
Zhen-Ru Zhang, Chuanqi Tan, Haiyang Xu +3
Fine-tuning large pre-trained language models on various downstream tasks with whole parameters is prohibitively expensive. Hence, Parameter-efficient fine-tuning has attracted att…
Knowledge Rumination for Pre-trained Language Models
Yunzhi Yao, Peng Wang, Shengyu Mao +4
Previous studies have revealed that vanilla pre-trained language models (PLMs) lack the capacity to handle knowledge-intensive NLP tasks alone; thus, several works have attempted t…