2.5k citations · 3.4k across the 28 of their papers we have counts for
43 papers
HyperTuning: Toward Adapting Large Language Models without Back-propagation
Jason Phang, Yi Mao, Pengcheng He +1
Fine-tuning large language models for different tasks can be costly and inefficient, and even methods that reduce the number of tuned parameters still require full gradient-based o…
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation
Biyang Guo, Yeyun Gong, Yelong Shen +4
We introduce GENIUS: a conditional text generation model using sketches as input, which can fill in the missing contexts for a given sketch (key information consisting of textual s…
Soft-Labeled Contrastive Pre-training for Function-level Code Representation
Xiaonan Li, Daya Guo, Yeyun Gong +6
Code contrastive pre-training has recently achieved significant progress on code-related tasks. In this paper, we present \textbf{SCodeR}, a \textbf{S}oft-labeled contrastive pre-t…
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval
Kun Zhou, Yeyun Gong, Xiao Liu +8
Sampling proper negatives from a large document pool is vital to effectively train a dense retrieval model. However, existing negative sampling strategies suffer from the uninforma…
A Self-Paced Mixed Distillation Method for Non-Autoregressive Generation
Weizhen Qi, Yeyun Gong, Yelong Shen +6
Non-Autoregressive generation is a sequence generation paradigm, which removes the dependency between target tokens. It could efficiently reduce the text generation latency with pa…
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation
Simiao Zuo, Qingru Zhang, Chen Liang +3
Pre-trained language models have demonstrated superior performance in various natural language processing tasks. However, these models usually contain hundreds of millions of param…