I Have an Attention Bridge to Sell You: Generalization Capabilities of Modular Translation Architectures

• URL: http://arxiv.org/abs/2404.17918v2
• Date: Tue, 30 Apr 2024 05:53:10 GMT
• Title: I Have an Attention Bridge to Sell You: Generalization Capabilities of Modular Translation Architectures
• Abstract summary: We study whether modularity affects translation quality; as well as how well modular architectures generalize across different evaluation scenarios. For a given computational budget, we find non-modular architectures to be always comparable or preferable to all modular designs we study.
• Score: 3.0696094577634963
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modularity is a paradigm of machine translation with the potential of bringing forth models that are large at training time and small during inference. Within this field of study, modular approaches, and in particular attention bridges, have been argued to improve the generalization capabilities of models by fostering language-independent representations. In the present paper, we study whether modularity affects translation quality; as well as how well modular architectures generalize across different evaluation scenarios. For a given computational budget, we find non-modular architectures to be always comparable or preferable to all modular designs we study.

Related papers

• Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation [59.37775534633868]
  We present an extremely straightforward approach to transferring pre-trained, task-specific PEFT modules between same-family PLMs. We also propose a method that allows the transfer of modules between incompatible PLMs without any change in the inference complexity.
  arXiv  Detail & Related papers  (2024-03-27T17:50:00Z)
• Unlocking Emergent Modularity in Large Language Models [27.12431620957652]
  We show that standard Language Models (LMs) could be fine-tuned as their Mixture-of-Expert (MoEs) counterparts without introducing any extra parameters. Our experiments demonstrate that fine-tuning EMoE effectively improves downstream in-domain and out-of-domain generalization compared with vanilla fine-tuning.
  arXiv  Detail & Related papers  (2023-10-17T01:02:32Z)
• Modularity in Deep Learning: A Survey [0.0]
  We review the notion of modularity in deep learning around three axes: data, task, and model. Data modularity refers to the observation or creation of data groups for various purposes. Task modularity refers to the decomposition of tasks into sub-tasks. Model modularity means that the architecture of a neural network system can be decomposed into identifiable modules.
  arXiv  Detail & Related papers  (2023-10-02T12:41:34Z)
• ModuleFormer: Modularity Emerges from Mixture-of-Experts [60.6148988099284]
  This paper proposes a new neural network architecture, ModuleFormer, to improve the efficiency and flexibility of large language models. Unlike the previous SMoE-based modular language model, ModuleFormer can induce modularity from uncurated data.
  arXiv  Detail & Related papers  (2023-06-07T17:59:57Z)
• Modular Deep Learning [120.36599591042908]
  Transfer learning has recently become the dominant paradigm of machine learning. It remains unclear how to develop models that specialise towards multiple tasks without incurring negative interference. Modular deep learning has emerged as a promising solution to these challenges.
  arXiv  Detail & Related papers  (2023-02-22T18:11:25Z)
• Language Models are General-Purpose Interfaces [109.45478241369655]
  We propose to use language models as a general-purpose interface to various foundation models. A collection of pretrained encoders perceive diverse modalities (such as vision, and language) We propose a semi-causal language modeling objective to jointly pretrain the interface and the modular encoders.
  arXiv  Detail & Related papers  (2022-06-13T17:34:22Z)
• Is a Modular Architecture Enough? [80.32451720642209]
  We provide a thorough assessment of common modular architectures, through the lens of simple and known modular data distributions. We highlight the benefits of modularity and sparsity and reveal insights on the challenges faced while optimizing modular systems.
  arXiv  Detail & Related papers  (2022-06-06T16:12:06Z)
• S2RMs: Spatially Structured Recurrent Modules [105.0377129434636]
  We take a step towards exploiting dynamic structure that are capable of simultaneously exploiting both modular andtemporal structures. We find our models to be robust to the number of available views and better capable of generalization to novel tasks without additional training.
  arXiv  Detail & Related papers  (2020-07-13T17:44:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.