An Architecture for Accelerated Large-Scale Inference of Transformer-Based Language Models (2021.naacl-industry)
Copied to clipboard
| Challenge: | a recent paper shows that attention-based language models can be used to train, evaluate, and perform inference on predictive models. |
| Approach: | They develop a machine learning architecture that can scale to a large volume of requests . they use a BERT model that is fine-tuned for emotion analysis . |
| Outcome: | The proposed architecture can scale to a large volume of requests with a minimum of 96 hours of running time. |
Similar Papers
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT (2021.tacl-1)
Copied to clipboard
Prakhar Ganesh, Yao Chen, Xin Lou, Mohammad Ali Khan, Yin Yang, Hassan Sajjad, Preslav Nakov, Deming Chen, Marianne Winslett
| Challenge: | Popular pre-trained Transformers have improved performance for various NLP tasks by sizable margins, but are too resource-hungry and computation-intensive to suit low-capacity devices or applications with strict latency requirements. |
| Approach: | They present a literature review of the compression of Transformers, focusing on the popular BERT model, which has attracted considerable research attention. |
| Outcome: | The proposed models improve Sentiment analysis, paraphrase detection, machine reading comprehension, question answering, text summarization, and other tasks by sizable margins. |
Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model (2022.naacl-industry)
Copied to clipboard
Gongzheng Li, Yadong Xi, Jingzhen Ding, Duan Wang, Ziyang Luo, Rongsheng Zhang, Bai Liu, Changjie Fan, Xiaoxi Mao, Zeng Zhao
| Challenge: | Recent studies show that transformer-based models are effective over many tasks, but they are expensive to deploy in the industrial application. |
| Approach: | They propose a transformer-based inference solution that optimizes kernels for long inputs and large hidden sizes and a flexible CUDA memory manager to reduce the memory footprint when deploying a large model. |
| Outcome: | The proposed solution achieves an average speedup of 1.40-4.20x on the transformer decoder layer with an A100 GPU. |
Layer-Condensed KV Cache for Efficient Inference of Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Using a key-value cache, memory consumption is a bottleneck for high-throughput language models. |
| Approach: | They propose a method that only computes and caches the KVs of a small number of layers, thus saving memory consumption and improving inference throughput. |
| Outcome: | The proposed method achieves higher throughput and competitive performance than standard transformers and is orthogonal to existing transformer memory-saving techniques. |
What Language Model to Train if You Have One Million GPU Hours? (2022.findings-emnlp)
Copied to clipboard
Teven Le Scao, Thomas Wang, Daniel Hesslow, Stas Bekman, M Saiful Bari, Stella Biderman, Hady Elsahar, Niklas Muennighoff, Jason Phang, Ofir Press, Colin Raffel, Victor Sanh, Sheng Shen, Lintang Sutawika, Jaesung Tae, Zheng Xin Yong, Julien Launay, Iz Beltagy
| Challenge: | Recent years have seen the advent of large language models characterized by emergent capabilities arising from sheer scale alone. |
| Approach: | They propose to use a multilingual model to compare performance to the English-only model by ablation at the billion-parameter scale. |
| Outcome: | The proposed model is based on a multilingual model and its performance against the English-only model. |
Evaluating Large Language Models for Belief Inference: Mapping Belief Networks at Scale (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Beliefs are interconnected, influencing how people process and update what they think. |
| Approach: | They propose to use a finetuned GPT-4o model to infer belief structures from large-scale social media data. |
| Outcome: | The proposed model can recover belief structures from large social media data, allowing for a level of scalability and efficiency that is impossible using traditional survey methods. |
GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples (2020.acl-main)
Copied to clipboard
| Challenge: | Recent Transformer-based architectures provide impressive results in many NLP tasks, but obtaining high-quality annotated data is expensive and time consuming. |
| Approach: | They propose a semisupervised learning method that ex- tends the fine-tuning of BERT-like architectures with unlabeled data in a generative adversarial setting. |
| Outcome: | The proposed method reduces the requirement for annotated examples while achieving good performance in sentence classification tasks. |
LightSeq: A High Performance Inference Library for Transformers (2021.naacl-industry)
Copied to clipboard
| Challenge: | Existing inference frameworks for natural language processing are not the best choice for online service of sequence processing problems. |
| Approach: | They propose a highly efficient inference library for Transformer models that includes GPU optimization techniques to streamline computation and reduce memory footprint. |
| Outcome: | The proposed library achieves 14x speedup compared with TensorFlow and 1.4x speed up compared to a concurrent CUDA implementation. |
Sharing Encoder Representations across Languages, Domains and Tasks in Large-Scale Spoken Language Understanding (2023.acl-industry)
Copied to clipboard
Jonathan Hueser, Judith Gaspers, Thomas Gueudre, Chandana Prakash, Jin Cao, Daniil Sorokin, Quynh Do, Nicolas Anastassacos, Tobias Falke, Turan Gojayev
| Challenge: | Larger encoders can improve accuracy for spoken language understanding (SLU) but are difficult to use given the inference latency constraints of online systems. |
| Approach: | They propose to use a larger 170M parameter BERT encoder that shares representations across languages, domains and tasks for SLU. |
| Outcome: | The proposed encoders achieve state-of-the-art performance on numerous NLP tasks. |
DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling (2024.naacl-long)
Copied to clipboard
| Challenge: | Rapid explosion in model sizes has resulted in high inference times . open-source LLMs are democratizing research in natural language processing . |
| Approach: | They propose a suite of multi-token prediction language models that reduce net inference times by leveraging traditional autoregressive weights. |
| Outcome: | The proposed model achieves same-quality generated text as baseline (Pythia-6.9B) with only 5.87% and 2.67% parameter and training time overheads. |
Dynamic and Efficient Inference for Text Generation via BERT Family (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods to improve performance of pre-trained language models are limited due to large-scale parameters and the universal autoregressive decoding paradigm. |
| Approach: | They propose a novel fine-tuning method which can make a single pre-trained model support Dynamic and Efficient infERence and achieve an adaptive trade-off between model performance and latency. |
| Outcome: | The proposed method achieves higher BLEU scores than the strong autoregressive Transformer model on translation tasks with 3 12 times speedup and faster inference speed compared with the BART model on four GLGE benchmark tasks. |