Search Results for author:
Found 14 papers, 9 papers with code
IrEne-viz: Visualizing Energy Consumption of Transformer Models
IrEne is an energy prediction system that accurately predicts the interpretable inference energy consumption of a wide range of Transformer-based NLP models.
OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
To this end, we release OpenELM, a state-of-the-art open language model.
APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference
Compared to baselines, our experiments show that APT maintains up to 98% task performance when pruning RoBERTa and T5 models with 40% parameters left while keeping 86. 4% LLaMA models' performance with 70% parameters remained.
BTR: Binary Token Representations for Efficient Retrieval Augmented Language Models
Retrieval augmentation addresses many critical problems in large language models such as hallucination, staleness, and privacy leaks.
Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation
In response, we introduce Pentathlon, a benchmark for holistic and realistic evaluation of model efficiency.
AdANNS: A Framework for Adaptive Semantic Search
Finally, we demonstrate that AdANNS can enable inference-time adaptivity for compute-aware search on ANNS indices built non-adaptively on matryoshka representations.
PuMer: Pruning and Merging Tokens for Efficient Vision Language Models
Large-scale vision language (VL) models use Transformers to perform cross-modal interactions between the input text and image.
A Survey for Efficient Open Domain Question Answering
Thus, a trade-off between accuracy, memory consumption and processing speed is pursued.
Efficient Methods for Natural Language Processing: A Survey
Recent work in natural language processing (NLP) has yielded appealing results from scaling model parameters and training data; however, using only scale to improve performance means that resource consumption also grows.
IrEne: Interpretable Energy Prediction for Transformers
We present IrEne, an interpretable and extensible energy prediction system that accurately predicts the inference energy consumption of a wide range of Transformer-based NLP models.
Bew: Towards Answering Business-Entity-Related Web Questions
We present a practical approach, called BewQA, that can answer Bew queries by mining a template of the business-related webpages and using the template to guide the search.
Towards Accurate and Reliable Energy Measurement of NLP Models
In this work, we show that existing software-based energy measurements are not accurate because they do not take into account hardware differences and how resource utilization affects energy consumption.
DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering
It turns out that we can get by without input-wide self-attention at all layers, especially in the lower layers.
MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU
In this paper, we explore optimizations to run Recurrent Neural Network (RNN) models locally on mobile devices.
