32 citations · 99 across the 23 of their papers we have counts for
7 papers · 1 filter
Adaptive Draft-Verification for Efficient Large Language Model Decoding
Xukun Liu, Bowen Lei, Ruqi Zhang +1
Large language model (LLM) decoding involves generating a sequence of tokens based on a given context, where each token is predicted one at a time using the model's learned probabi…
ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models
Binfeng Xu, Zhiyuan Peng, Bowen Lei +3
Augmented Language Models (ALMs) blend the reasoning capabilities of Large Language Models (LLMs) with tools that allow for knowledge retrieval and action execution. Existing ALM s…
A Survey for Efficient Open Domain Question Answering
Qin Zhang, Shangsi Chen, Dongkuan Xu +4
Open domain question answering (ODQA) is a longstanding task aimed at answering factual questions from a large knowledge corpus without any explicit evidence in natural language pr…
An Automatic and Efficient BERT Pruning for Edge AI Systems
Shaoyi Huang, Ning Liu, Yueying Liang +5
With the yearning for deep learning democratization, there are increasing demands to implement Transformer-based natural language processing (NLP) models on resource-constrained de…
AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models
Dongkuan Xu, Subhabrata Mukherjee, Xiaodong Liu +5
Knowledge distillation (KD) methods compress large models into smaller students with manually-designed student architectures given pre-specified computational cost. This requires s…
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
Shaoyi Huang, Dongkuan Xu, Ian E. H. Yen +8
Conventional wisdom in pruning Transformer-based language models is that pruning reduces the model expressiveness and thus is more likely to underfit rather than overfit. However,…
Rethinking Network Pruning -- under the Pre-train and Fine-tune Paradigm
Dongkuan Xu, Ian E. H. Yen, Jinxi Zhao +1
Transformer-based pre-trained language models have significantly improved the performance of various natural language processing (NLP) tasks in the recent years. While effective an…