Efficient nlp. IIE-NLP group is led by Prof.
Efficient nlp She had published over 60 papers on top conferences of AI and NLP, including ACL, Oct 13, 2022 · Recent work has observed that pre-trained models have higher out-of-distribution (OOD) robustness when they are exposed to less in-distribution (ID) training data (Radford et al. huji. Oct 13, 2021 · The proposed ELUE has a strong Pareto Frontier and makes a better evaluation for efficient NLP models, and a strong baseline, ElasticBERT, which allows BERT to exit at any layer in both static and dynamic ways is released. Although the Mambular-Triton implementation If you find the code useful, please cite the following papers: Efficient NLP Model Finetuning via Multistage Data Filtering. This trend raises the bar for participation in NLP research, excluding large parts of the community from experimenting with state-of-the-art models. 1 contributor; History: 4 commits. Model card Files Files and versions Community Train Deploy Use in Transformers. BERT employs several innovative techniques that contribute to its performance: Bidirectional Contextualization: Unlike traditional models, BERT processes text bidirectionally, allowing it to understand the context of a word based on both its preceding and following words. cn 31 Oct 2021. stanford. As the field of NLP continues to grow, the focus is no Jan 23, 2023 · ubiquitous in various natural language processing (NLP) ap-plications, including machine translation [2], question answer-ing and document analysis [3]. optimization, theory and NLP Aug 30, 2024 · 文章浏览阅读1. Gse is implements jieba by golang, and try add NLP support and more feature 5 days ago · %0 Conference Proceedings %T The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment %A Fernandez, Jared %A Kahn, Jacob %A Na, Clara %A Bisk, Yonatan %A Strubell, Emma Feb 10, 2021 · Efficient NLP Full-Stack Innovations. Our objective is to make the parameters and behaviour of LLMs more explainable and understandable to both end users and researchers. State-of-the-art language models in NLP perform best when fine-tuned even on small datasets, but due to their increasing size, finetuning and downstream usage have become extremely compute-intensive. main teochew-whisper-medium / README. To this end, we set to filter training examples in a streaming fashion, in tandem with training the target model. 4. Top Information Retrieval Papers of Aug 2, 2024 · Efficient federated learning for NLP: reduce the communication costs, tackling heterogeneous data, heterogeneous models. j p Phi l B l unsom , Oxford Uni ve rsi t y & De e pm i nd, pbl unsom @ googl e . Such resources include data, time, storage, or energy, all of which are naturally limited and unevenly distributed. Sep 22, 2023 · In recent years, deep neural networks have achieved excellent performance in a variety of natural language processing (NLP) tasks []. PyTorch. , FedNLP). New transformer architectures that promise less compute-intense NLP training, in order of my excitement to use them: ⚡ Reformer: The Efficient Transformer ⚡. However, one piece of notable work includes the proposal of a hash-based clustering technique for learning binary embed- Dec 19, 2023 · Abstract page for arXiv paper 2312. Such capabilities, however, come with the considerable resources they demand, highlighting the strong need to develop effective Jan 3, 2025 · NVIDIA researchers have unveiled Hymba 1. As the demand for real-time, personalized AI experiences grows, the need for efficient processing on mobile devices becomes paramount. like 20. edu robinjia@usc. Rather than pursuing the reachless SOTA accuracy, most works are pursuing improvement on other dimensions such as efficiency, leading to "Pareto SOTA". Dec 1, 2024 · Efficiency [115, 7] NLP techniques enhance the efficiency of text summarization by quickly processing large volumes of data, allowing users to obtain critical insights rapidly. Liu ♠Ananya Kumar Percy Liang Robin Jia♡ ♠Computer Science Department, Stanford University, Stanford, CA ♡Department of Computer Science, University of Southern California, Los Angeles, CA {nfliu, ananya, pliang}@cs. Zheng Lin, who received her PhD degree from the Institute of Computing Technology, CAS in 2014. Xiangyang Liu, Tianxiang Sun*, Junliang He, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng Qiu. However, training these large dense models requires significant amounts of computing resources. Jaeyong Song, Jinkyu Yim, Jaewon Jung, Hongsun Jang, Hyung-Jin Kim, Youngsok Kim, Jinho Lee. Scope of this Survey We address this work to two groups of readers: (1) Researchers from all fields of NLP working with limited resources; and (2) Researchers interested in improving the state of the art of efficient methods 4 days ago · Towards Efficient NLP A Standard Evaluation and A Strong Baseline Tianxiang Sun txsun19@fudan. Automated sentiment analysis can solve the problem of classifying the sentiment of user comments and opinions in a big data model and has a high commercial value []. Processing large datasets can be computationally expensive, requiring significant resources and time. Rather than Jul 12, 2023 · This survey synthesizes and relates current methods and findings in efficient NLP. The original implementation of KAN is available here. Reformer enables training Oct 10, 2024 · Summary: Small Language Models (SLMs) are transforming the AI landscape by providing efficient, cost-effective solutions for Natural Language Processing tasks. KD for model compression and study of use of adversarial training to improve student accuracy using just the logits of the teacher as in standard KD. the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023) Feb 26, 2020 · DIFFUSION FOR TRAINING EFFICIENT NLP MODELS Anonymous authors Paper under double-blind review ABSTRACT We propose R2D2 layers, a new neural block for training efficient NLP models. ), the issue of energy efficiency in NLP becomes more apparent Dec 20, 2023 · Efficient NLP Inference at the Edge via Elastic Pipelining (ASPLOS’23) Liwei Guo, Wonkyo Choe, Felix Lin Rethinking Remote Memory Placement on Large-Memory Systems with Path Diversity (ApSys’21) Wonkyo Choe*, Sang-Hoon Kim, Jeongseob Ahn Exploring the Design Space of Page Management for Multi-Tiered Memory Systems (ATC’21) Jun 20, 2024 · 与完全微调相比，仅仅增加了3. Oct 21, 2024 · IIE-NLP. Dec 31, 2024 · Whether you’re a developer looking for lightweight solutions or a researcher exploring efficient NLP, this list highlights models that prove that bigger isn’t always better. 04451: Reformer: The Efficient Transformer Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. Speaker: Dr. 6%的参数，就接近了SOTA的结果。_parameter-efficient transfer learning for nlp 【论文笔记】Parameter-Effificient Transfer Learning for NLP 最新推荐文章于 2024-10-14 10:34:49 Apr 22, 2022 · Parameter-efficient transfer learning for NLP. Instant dev environments Go efficient multilingual NLP and text segmentation; support English, Chinese, Japanese and others. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. By Figure 2: Schematic overview of the efficient NLP stages covered in this paper, starting with data collection and model design, followed by training and inference, and ending with evaluation and model selection. July 09, 2021 | BY roys02 . Rather than pursuing the reachless SOTA accuracy, more and more Jan 14, 2022 · SpAtten is an efficient algorithm-architecture co-design that leverages token sparsity, head sparsity, and quantization opportunities to reduce the attention computation and Oct 13, 2021 · Supersized pre-trained language models have pushed the accuracy of various natural language processing (NLP) tasks to a new state-of-the-art (SOTA). We recommend using Dec 8, 2023 · different steps in the NLP pipeline, by providing a detailed overview of efficiency methods spe-cific to NLP (Figure 2). Requirements. 12430: Efficient Title Reranker for Fast and Improved Knowledge-Intense NLP. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural May 4, 2023 · 文章浏览阅读967次，点赞2次，收藏2次。微调大型预训练模型是NLP中一种有效的传递机制。然而，在存在许多下游任务的情况下，微调是参数效率低下的：每个任务都需要一个全新的模型。作为替代方案，我们建议使用适 Nov 3, 2021 · The amount of computation put into training NLP models has grown tremendously in recent years. Bhavana Dalvi Mishra is a Lead Research Scientist at the Allen Institute for AI (Ai2). One major field, underexplored in the neuromorphic setting, is Natural Language Processing (NLP), where most state-of-the-art solutions still heavily rely on resource-consuming and power-hungry Efficient, Extensible kNN-MT Framework. While architectures like transformers and Mamba, originally designed for NLP, have been adapted Towards Efficient NLP: A Standard Evaluation and A Strong Baseline. With, the adoption of techniques from natural language Contribute to eole-nlp/eole development by creating an account on GitHub. 1 Sep 19, 2023 · Towards Efficient NLP: A Standard Evaluation and A Strong Baseline. The performance issue of the original implementation is mostly because it needs to expand all intermediate variables to perform the different activation functions. Yu♡ ♣ Central South University ♠ Harbin Institute of Technology ♢ University of Hong Kong ♮ Tsinghua University ♡ University of Illinons at Chicago lbqin@csu. 4k次，点赞15次，收藏22次。如何提高大语言模型（LLM）在多跳问答系统中的检索效率？论文提出了EfficientRAG框架，通过迭代生成新查询并减少对LLM的依赖，提高了检索效率和问答准确性。_efficientrag: efficient retriever for multi If you find the code useful, please cite the following papers: Efficient NLP Model Finetuning via Multistage Data Filtering. This synergy allows for the seamless incorporation of Nov 26, 2024 · Recent advancements in tabular deep learning (DL) have led to substantial performance improvements, surpassing the capabilities of traditional models. ELUE is dedicated to depicting the Pareto Front for various language understanding tasks, such that it can tell whether and how much a method achieves Pareto improvement. Text classification is a fundamental task in NLP, where the goal is to categorize text into predefined labels. for domains or . CCNET: Criss-cross Feb 1, 2024 · NLP research as the needs for more dense representations of text have become more apparent. Our proposed method is characterized by a dynamic weight diffusion mechanism which learns to reuse and reduce parameters in the conventional 6 days ago · Future research directions include further refining the model’s architecture to improve both its capacity and efficiency, analyzing in depth how lower-level language models scale with increasing sizes and data volume, as well as extending the context length to seamlessly process diverse data types – images, videos, and audio – simultaneously. Nov 6, 2024 · I have focused on building efficient and practical NLP systems for both edge devices and the cloud, such as on-device (visual) question answering and faster Transformer models. Abstract Recent work in natural language processing (NLP) has yielded appealing results from scaling model Aug 1, 2024 · The E&E group of DFKI’s Research Department Multilinguality and Language Technology works on transparent and efficient NLP models. osa ka -u. We aim to provide both guidance for Jul 12, 2023 · Abstract. Jul 11, 2022 · Natural Language Processing (NLP) inference is seeing increasing adoption by mobile applications, where on-device inference is desirable for crucially preserving user data privacy and avoiding network roundtrips. Large pre-trained language models based on transformer architectureƒhave drastically changed the natural language processing (NLP) Jan 14, 2020 · Abstract page for arXiv paper 2001. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1689–1709, Toronto, Canada. Aug 1, 2021 · In book: Knowledge Science, Engineering and Management, 14th International Conference, KSEM 2021, Tokyo, Japan, August 14–16, 2021, Proceedings, Part II (pp. 2019. Feb 3, 2019 · Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. Dec 6, 2024 · In conclusion, the field of Natural Language Processing (NLP) has significantly transformed the way humans interact with machines, enabling more intuitive and efficient communication. That requires innovations across the full stack, from algorithm to hardware. Resources. Motivated by redundancy in training examples and the sheer sizes of pretrained models, we exploit a key opportunity: training only on important data. 简体中文. You signed out in another tab or window. NAACL 2022. Let’s dive in and discover how small models are Nov 3, 2021 · Efficient NLP Yuki Ara se , Osa ka Uni ve rsi t y, a ra se @ i st . Oct 20, 2024 · Efficient NLP org 5 days ago @ dhatta Probably not for Teochew, since that is an English-only model, and having a model with Chinese knowledge is important. The Era of Big Models. Being able to efficiently and effectively fine-tune the largest pre Jul 13, 2023 · pment of more efficient NLP models; and (2) providing simpler architectures and empirical justification of model complexity. like 3. See translation. ChatGPT, LLaMA, etc. In Proceedings of ICML (2019). 6%的参数，就接近了SOTA的结果。 [论文阅读72]Parameter-Efficient Transfer Learning for NLP Oct 2, 2023 · Transformer-based pre-trained models have revolutionized NLP for superior performance and generality. This trend raises the bar for participation in NLP This repository contains an efficient implementation of Kolmogorov-Arnold Network (KAN). Motivated by re-dundancy in training examples and the Dec 22, 2021 · Combined compression techniques for more efficient NLP and speech models; Efficient KD for NLP and speech, efficient intermediate layer distillation, and teacher-free distillation; Improving KD for large classification A working group appointed by the ACL Executive Committee to promote ways that the ACL community can reduce the computational costs of NLP and thereby mitigate some of these concerns. This trend raises the bar for participation in NLP research, excluding large parts Jul 28, 2022 · This work automatically determines a training loss threshold for skipping backward training passes and runs a meta predictor for further skipping forward training passes, and integrates the above techniques in a holistic, three-stage training pro- cess. Edit Preview. This capability is crucial Dec 3, 2024 · Here, we explore several applications of efficient NLP transfer learning, highlighting its impact across various domains. , Adapters, Prefix-tuning) have achieved parameter-efficiency, the computational and memory costs with these tuning techniques are still high. Different from accuracy, the metric for efficiency varies across different studies, 5 days ago · Are Sample-Efficient NLP Models More Robust?. The codes will be updated soon. PR & discussions documentation; Working with and contributing to the open source community in data mining, artificial intelligence, and related fields. 5B, an open-source language model that combines transformer and state-space model (SSM) architectures to achieve unprecedented efficiency and performance 6 days ago · Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. With the increasing complexity of NLP tasks, there is a growing need for models that are not only Jul 12, 2023 · This motivates research into efficient methods that require fewer resources to achieve similar results. ICLR, along with ICML and NeurIPS, are three of the most influential and widely followed conferences in the fields of machine learning and artificial intelligence. This year, we received 46 submissions, proposing a multitude of viable resource-efficient NLP methods and spanning a wide range of NLP applications. However, fine-tuning all weights of models with millions or billions of parameters is sample-inefficient, unstable in low-resource settings, and wasteful as it requires storing a separate Dec 6, 2023 · Large Language Models (LLMs) have demonstrated remarkable capabilities in important tasks such as natural language understanding and language generation, and thus have the potential to make a substantial impact on our society. Inference Endpoints. Despite the progress, those giant models still rely on in-domain data to work well in down-stream tasks, which is hard and costly to obtain in Jan 22, 2022 · 来自：NLP日志提纲1简介2 Adapter3 Adapter fusion4总结参考文献1 简介目前在大规模预训练模型上进行finetune 是NLP中一种高效的迁移方法，但是对于众多的下游任务而言，finetune是一种低效的参数更新方式，对于每一个下游任务，都需要去 Efficient NLP 2. Mar 4, 2022 · 文章浏览阅读3. g. You switched accounts on another tab or window. ELECTRA is being released efficient-nlp / teochew-whisper-medium. Sumit. In this survey, we focus on the inference stage and review the current state of model compression and acceleration for pretrained language models, including Dec 13, 2024 · Title: (KeyNote Talk) Efficiency through Learning from Experience Presenter: Dr. Parametric and retrieval-augmented models have May 16, 2023 · 自然语言处理(NLP)帮助智能机器更好地理解人类语言，实现基于语言的人机交流。计算能力的最新发展和大量语言数据的出现，增加了使用数据驱动方法自动进行语义分析的需求。由于深度学习方法在计算机视觉、自动语音 Aug 19, 2023 · As model finetuning is central to the modern NLP, we set to maximize its efficiency. 2) Embedding Binarization: While binarization efforts fall under efficient NLP, it is also a relatively underexplored field. bl unsom @ c s. md. 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. In summary, Low-Rank Adaptation techniques represent a promising avenue for enhancing the efficiency of foundational models. cn, Oct 30, 2022 · Access to external knowledge is essential for many natural language processing tasks, such as question answering and dialogue. e du Je sse Dodge , Al l e n Inst i t ut e for AI, j e sse d@ a l l e na i . Documentation: Enhance and expand the documentation for better user guidance. whisper. Contact: Roy Schwartz. Yet, the unprecedented size of an NLP model stresses both latency and memory, creating a tension between the two key resources of a mobile Nov 1, 2021 · A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range dependencies. org Nov 24, 2023 · 🔍 This is the official code and data repository for the EMNLP 2023 paper titled "Unlearn What You Want to Forget: Efficient Unlearning for LLMs". From SOTA to “Pareto SOTA” •The Shifted Goal •Instead of pursuing the reachless SOTA accuracy, most works are pursuing improvement on other dimensions (like efficiency), leading to Pareto SOTA. a c . Continue enhancing the codebase for clarity and efficiency. 139-151) 4 days ago · This methodological shift in how data is processed underscores a broader trend in NLP toward more efficient models. Transformers. Our goal is to Saved searches Use saved searches to filter your results more quickly Efficient model finetuningModern NLP models are pre-trained on large corpora once and then finetuned for specific domains. Zichao Yang. Contribute to haryoa/awesome-efficient-nlp development by creating an account on GitHub. Data Efficiency Pre-trained models rely on a huge amount of unlabeled data which makes the Oct 15, 2021 · To that end, this work presents ELUE (Efficient Language Understanding Evaluation), a standard evaluation, and a public leaderboard for efficient NLP models. Automatic Speech Recognition. 1. Motivated by redundancy in May 5, 2020 · Efficient NLP - Transformers. One approach is to use smaller and simpler models (distill Dec 19, 2023 · With immense potential to optimize organizational decision-making, this pioneering research intersections automation, NLP and knowledge management to elevate workplace efficiency. Feb 15, 2022 · Efficient NLP research aims to comprehensively consider computation, time and carbon emission for the entire life-cycle of NLP, including data preparation, model training and inference. In International Conference on Machine Oct 13, 2022 · Recent results in image classification and extractive question answering have observed that pre-trained models trained on less in-distribution data have better out-of-distribution performance. Association for We are the Natural Language Processing Lab at Duke University. Researchers proposed various automatic testing techniques for adversarial test cases. For both aspects, we encouraged submissions from all topical areas of NLP. edu Abstract As model netuning is central to the modern NLP, we set to maximize its efciency. In this paper, we propose and Jun 8, 2021 · Adapting large-scale pretrained language models to downstream tasks via fine-tuning is the standard method for achieving state-of-the-art performance on NLP benchmarks. md Jun 1, 2023 · Are Sample-Efficient NLP Models More Robust? Nelson F. By focusing on optimizing the training process and minimizing the Jun 6, 2023 · Therefore, researchers are exploring alternative methods that can provide accurate and efficient NLP solutions without relying on LLMs. Notably, the training stage is divided into two parts: pre-training, which aims to learn generalizable parameters, and fine-tuning, which optimizes these parameters for specific Jan 15, 2025 · The deployment of lightweight NLP models for edge devices presents unique challenges and opportunities. These models though effective in many NLP tasks, however, Feb 22, 2024 · This repository is a collection of Knowledge Distillation (KD) methods implemented by the Huawei Montreal NLP team. Dr. This survey synthesizes and relates current methods and findings in Oct 13, 2021 · Supersized pre-trained language models have pushed the accuracy of various NLP tasks to a new state-of-the-art (SOTA). Nov 10, 2024 · 克服微调训练不高效的问题，增加一些adapter模块，思想就是固定原始的网络中的参数，针对任务增加一些可以训练的参数，新任务无需重新访问以前的任务，产生高度的参数共享。与完全微调相比，仅仅增加了3. Although tabular datasets Aug 31, 2022 · This survey synthesizes and relates current methods and findings in efficient NLP to provide both guidance for conducting NLP under limited resources, and point towards promising research directions for developing more efficient methods. * PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting, NAACL Findings 2022. In recent RAG approaches, rerankers play a pivotal role in refining retrieval accuracy with the ability of revealing logical relations for each pair of query and text. Apr 4, 2022 · Efficient NLP Yuki Ara se , Osa ka Uni ve rsi t y, a ra se @ i st . NLP encompasses a wide range of Feb 27, 2024 · As we see typical Natural Language Processing (NLP) architectures requiring vast amounts of computational power (e. However, existing rerankers Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression. c om Mona Di a b, Ge orge Wa shi ngt on Uni ve rsi t y & Fa c e book AI, m t di a b@ gwu. 5 days ago · This motivates research into efficient methods that require fewer resources to achieve similar results. The amount of computation put into training NLP models has grown tremendously in recent years. Abstract: Recent advancements in tabular deep learning (DL) have led to substantial per-, formance improvements, surpassing the capabilities of traditional models. Text Classification. Conclusion. 1k次，点赞7次，收藏21次。这篇文章可以作为入门Word2vec的一篇论文，文章发表于2013年，其提供了NLP发展至今过程中比较著名的词向量模型之一，即skip-gram和CBOW模型。参考：①B站视频②论 May 21, 2022 · Transformer-based pre-trained models have revolutionized NLP for superior performance and generality. Transfer learning enhances this process by utilizing pre-trained models like BERT and GPT Dec 3, 2024 · These innovations in parameter-efficient transfer learning techniques are crucial for developing efficient NLP models that can handle large volumes of complex data without incurring prohibitive computational costs. Both Adapters and Prefix-tuning methods causes changes to the PLM’s intermediate activations, thus the frozen PLM modules are still in the backpropagation pass during training Dec 1, 2024 · In this work, we addressed the key challenges that hinder the adoption of SNNs in energy-efficient NLP applications, specifically the low training efficiency and the absence of suitable parallelized spiking neurons. Xu Ouyang, Shahina Mohd Azam Ansari, Felix Xiaozhu Lin, Yangfeng Ji. Existing methods often rely on a parametric model that stores knowledge in its parameters, or use a retrieval-augmented model that has access to an external knowledge source. Although conventional models including RNNs, CNNs, and Transformers have specialized variants for capturing long dependencies, they still struggle to scale to very long This work introduces the pNLP-Mixer architecture, an embedding-free MLP-Mixers model for on-device NLP that achieves high weight-efficiency thanks to a novel projection layer, and consistently beats the state-of-the-art of tiny models. The model size and computation of NLP models are increasing exponentially. Dec 20, 2024. IIE-NLP group is led by Prof. Jan 2, 2025 · This makes it an attractive option for applications in efficient transfer learning in NLP, where rapid adaptation to new tasks is essential. The full name of ICLR 2025 is the 13th International Conference on Learning Representations. Model card Files Files and versions Community 1 Train Deploy Use this model New discussion New pull request. Zhongwei Wan 1, Xin Wang 1, Che Liu 2, Samiul Alam 1, Yu Zheng 3, Jiachen Liu 4, Zhongnan Qu 5, Shen Yan 6, Yi Zhu 7, Quanlu Zhang 8, Mosharaf Chowdhury Mar 10, 2020 · ELECTRA’s excellent efficiency means it works well even at small scale — it can be trained in a few days on a single GPU to better accuracy than GPT, a model that uses over 30x more compute. , GPT-3 and CLIP) have higher robustness than conventionally fine-tuned models, but these robustness gains fade as zero-shot models are Oct 20, 2022 · depicts these three representative parameter efficient tuning models. , 2021; Nori et al. Bhavana Dalvi Mishra Bio. Aug 23, 2022 · I made several changes to his code which used Hugging Face Trainer, switching the backbone to deberta-v3-large (the current state-of-the-art for NLP models), adding our W&B Integration for experiment tracking, and Jan 10, 2025 · The TKD-NLP model integrates Transformer architecture with knowledge distillation (KD) technology to optimize model efficiency while preserving the expressive power of Transformers. - DAMO-NLP-SG/Inf-CLIP You signed in with another tab or window. For example, thanks to scaling, GPT-3 was able to achieve strong results on in-context learning tasks. Aug 10, 2023 · Efcient NLP Model Finetuning via Multistage Data Filtering Xu Ouyang, Shahina Mohd Azam Ansari, Felix Xiaozhu Lin, Yangfeng Ji University of Virginia {ftp8nr, dtf8qc, felixlin, yangfeng}@virginia. Event Notification Type: Other. - HUAWEI Noah's Ark Lab Oct 13, 2021 · To that end, this work presents ELUE (Efficient Language Understanding Evaluation), a standard evaluation, and a public leaderboard for efficient NLP models. ox. Proceedings of the Thirty-Second Aug 7, 2021 · Natural language processing (NLP) tasks including machine translation [], speech recognition [] and sentiment analysis [] have over the years produced excellent results by employing Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) [], and Gated Recurrent Unit (GRU) models. Authors: Marcos Treviso, Ji-Ung Lee, Tianchu Ji, Betty van Aken, Qi 6 days ago · Supersized pre-trained language models have pushed the accuracy of various natural language processing (NLP) tasks to a new state-of-the-art (SOTA). ac. e du Jul 28, 2022 · As model finetuning is central to the modern NLP, we set to maximize its efficiency. Authors: Jaeyong Song, Jinkyu Yim, Jaewon Jung, Hongsun Jang, + 3, Hyung-Jin Kim, Youngsok Kim, Jinho Lee (Less) Authors Info & Claims. Model card Files Files and versions Community 4 Train Deploy Use this model main teochew-whisper-medium. Xiangyang Liu, Tianxiang Sun, Junliang He, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng Qiu. Contribute to naist-nlp/knn-seq development by creating an account on GitHub. Our key techniques 4 days ago · In January 2025, NLP Group has 2 papers accepted by ICLR 2025. Efficient finetuning is crucial because (1) as op-posed to one-off pretraining, finetuning is invoked for every downstream Nov 1, 2024 · Although existing PETL methods (e. Contact Email: roys@cs. On the right side of the figure, each operation method of Adapter, Prefix Tuning, and LoRA is described from the top. 83 for Dec 16 - Dec 22, 2024. MATE-KD. il. Google Scholar [30] Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu. Dear ACL Members, The amount of computation put into training NLP models has grown tremendously in recent years. However, deploying those models for on-device applications in constrained devices such as smart watches is completely impractical due to their size and inference cost. The third version of the Efficient Natural Language and Speech Processing (ENLSP-III) workshop will focus on the future of large language and speech foundation models; and how to make them more efficient in terms of Data, Model, Training, and Inference for real-world applications as well as academic research. Fine-tuning pre-trained models for downstream tasks often requires private data, for which federated learning is the de-facto approach (i. Included Projects. And supports with elasticsearch and bleve. Supersized pre-trained language models have pushed the accuracy of various natural language processing (NLP) tasks to a new state-of-the Nov 16, 2023 · 在AdapterLayer类中的add_adapter函数，往每一层中添加了Adapter或者是ParallelAdapter。通过add_adapter调用模型父类ModelAdaptersMixin的add_adapter方法实现增加adapter。3. Upload images, audio, and videos by Dec 14, 2021 · Scaling language models with more data, compute and parameters has driven significant progress in natural language processing. , 2019). Automatic Speech Recognition Transformers PyTorch whisper Inference Endpoints. Her research focuses on 3 days ago · Parameter-Efficient Fine-Tuning for NLP Models Wednesday, April 26, 2023 . edu Abstract Recent results in image PDF | On Jan 1, 2022, Xiangyang Liu and others published Towards Efficient NLP: A Standard Evaluation and A Strong Baseline | Find, read and cite all the research you need on ResearchGate Aug 22, 2022 · 而adapter fusion则跟MOE很像，可以联合多个任务的信息，从而提升特定任务的性能。但相比于其他的parameter-efficient的方法，adapter是在原语言模型上加入了新的模块，在推理时会进一步增加延迟。 参考文献 1. Abstract: In recent years, advances in deep learning for NLP research have been mainly propelled by massive computation and large amounts of data. Compared with traditional recurrent neural network (RNN) and long short-term memory (LSTM) [4], transformer-based model uses multi-head self-attention mechanism to encode and decode the information Nov 11, 2023 · [distributed training framework] Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression. e. edu. Efficiency analyses Aug 22, 2023 · The widespread adoption of DNNs in NLP software has highlighted the need for robustness. May 22, 2024 · NLP Large Language Models Meet NLP: A Survey Libo Qin♣ Qiguang Chen♠ Xiachong Feng♢ Yang Wu♠ Yongheng Zhang♣ Yinghui Li♮ Min Li♣ Wanxiang Che♠ Philip S. This trend raises the bar for participation in NLP research, excluding large parts of the May 18, 2023 · As model finetuning is central to the modern NLP, we set to maximize its efficiency. Large language models (LLMs) have 4 days ago · LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token (Shaolei Zhang, Qingkai Fang, Zhe Yang, Yang Feng) 简介: GPT-4o等实时多模态大模型（LMM）的出现引发了人们对高效LMM的关注。 Nov 26, 2024 · It examines the transferability of efficient NLP architectures to the tabular domain, revealing that while the Mamba architecture is highly effective for text, its efficiency does not translate as well to tabular tasks, specially for the common cases in tabular domain with less than fifty features. This blog discusses their advantages, challenges, and the promising future of these Find and fix vulnerabilities Codespaces. 6% for BERT-based NLP software, and time Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Reload to refresh your session. ASPLOS’23 论文地址: Jan 31, 2024 · As spiking neural networks receive more attention, we look toward applications of this computing paradigm in fields other than computer vision and signal processing. With innovations in model compression and transfer learning, SLMs are being applied across diverse sectors. We aim to provide both guidance for conducting NLP under limited resources, and point towards promising Jan 23, 2023 · Efficient NLP Models Current efficient NLP models can be roughly categorized as two streams: model compression (static methods) and condi-tional computation (dynamic Dec 1, 2022 · We discussed a recent survey paper (co-written by Roy) that presents a broad overview of existing methods to improve NLP efficiency through the lens of traditional NLP 3 days ago · Efficiency in transformer models has been another key research focus. However, our measurements show that FedNLP is prohibitively slow due to the large model sizes and the Mar 29, 2022 · Towards efficient NLP models. By integrating retrieval systems with generative models, LLMQuoter effectively bridges the gap between larger, more resource-intensive systems and smaller, more efficient ones. Adapter modules yield a compact and extensible model; they add only a few Nov 30, 2024 · Real-World Applications in industries like healthcare, e-commerce, and law are already benefiting from the efficiency of modern NLP models. Share this post. j p Phi l B l unsom , Oxford Uni ve rsi t y, phi l . However, it is unclear how broadly these trends hold. With the adoption of techniques from natural language processing (NLP), such as language model-based approaches, DL models for tabular data have also grown in complexity and size. Transformer architecture-based models such as ELECTRA and GPT Feb 9, 2022 · Large pre-trained language models based on transformer architecture have drastically changed the natural language processing (NLP) landscape. To address the high training memory overhead problem, we propose individual-based coding method, which aligns SNN time-steps with Oct 18, 2024 · The official CLIP training codebase of Inf-CL: "Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss". . Automation can increase productivity, freeing time for more 5 days ago · Efficient NLP Model Finetuning via Multistage Data Filtering. We try to improve LLMs with regard to data consumption, e. May 17, 2024 · TO-DO. Efficient NLP Model Finetuning via Multistage Data Filtering Xu Ouyang, Shahina Mohd Azam Ansari, Felix Xiaozhu Lin, Yangfeng Ji. This motivates 5 days ago · As an alternative to transformer-based architectures, recent work on efficient NLP has shown that weight-efficient models can attain competitive performance for simple tasks, such as slot filling and intent classification, with Dec 20, 2024 · A Next-Generation Encoder for Fast and Efficient NLP, LLMs for Real-World Recommender Systems, and More! Vol. This survey synthesizes and relates current methods and findings in efficient NLP. We conduct a large empirical study across three tasks, three broadly-applicable modeling interventions (increasing model This document is the report of a working group appointed by the ACL Executive Committee to promote ways that the ACL community can reduce the computational costs of NLP and thereby mitigate some of these concerns. , 2021). luckyt Fix example code for current version of huggingface Dec 25, 2024 · 文章浏览阅读1. Test Coverage: Improve testing to ensure code reliability and performance. As an alternative to transformer-based Efficient Large Language Models: A Survey (Version 1: 12/06/2023; Version 2: 12/23/2023; Version 3: 01/31/2024; Version 4: 05/23/2024, camera ready version of Transactions on Machine Learning Research). However, existing methods suffer from two limitations: weak error-discovering capabilities, with success rates ranging from 0% to 24. This process is crucial for deploying efficient NLP transformer models in resource-constrained environments. Dec 13, 2024 · Title: On the Efficiency of NLP-Inspired Methods for Tabular Deep Learning Authors: Anton Frederik Thielmann, Soheila Samiee. Jan 13, 2025 · Efficient NLP Transformer Model Techniques. As model finetuning is central to the modern NLP, we set to maximize its efficiency. A super memory-efficiency CLIP training scheme. In particular, zero-shot models (e. Our research focuses on developing efficient and effective natural language processing models to fight misinformation and improve human-computer interaction. Parameter-efficient transfer learning for nlp. As an alternative, we propose transfer with adapter modules. 3k次，点赞51次，收藏3次。然而，在实际应用中，序列生成问题常常涉及大型动作空间（例如词汇表）和长动作序列（例如翻译），这对探索过程提出了严重的计算挑战，也是设计复杂采样方法的重要动机。此外，通过改进采样策略，可以平衡探索和利用，从而提高模型的长期奖励 Jul 9, 2021 · Efficient NLP Survey . License: mit. uk Mona Di a b, Ge orge Wa shi ngt on Uni ve rsi t y & Fa c e book AI, m t di a b@ gwu. the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023) Nov 27, 2024 · memory-efficiency and speed will play a crucial role, since GBDTs are not only performant but also highly efficient compared to tabular deep learning models (Gu et al. Previously, I was a postdoc in the UW NLP efficient-nlp / teochew-whisper-medium. luckyt Update README. kgy lijqi almd bxaex axdth mfh goajlx efbmr uypq hrkoef