Language Evolution with Deep Learning

• URL: http://arxiv.org/abs/2403.11958v1
• Date: Mon, 18 Mar 2024 16:52:54 GMT
• Title: Language Evolution with Deep Learning
• Authors: Mathieu Rita, Paul Michel, Rahma Chaabouni, Olivier Pietquin, Emmanuel Dupoux, Florian Strub,
• Abstract summary: Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language. This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models.
• Score: 49.879239655532324
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language within a simulated controlled environment. Several methods have been used to investigate the origin of our language, including agent-based systems, Bayesian agents, genetic algorithms, and rule-based systems. This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models. The chapter introduces the basic concepts of deep and reinforcement learning methods and summarizes their helpfulness for simulating language emergence. It also discusses the key findings, limitations, and recent attempts to build realistic simulations. This chapter targets linguists and cognitive scientists seeking an introduction to deep learning as a tool to investigate language evolution.

Related papers

• Finding Structure in Language Models [3.882018118763685]
  This thesis is about whether language models possess a deep understanding of grammatical structure similar to that of humans. We will develop novel interpretability techniques that enhance our understanding of the complex nature of large-scale language models.
  arXiv  Detail & Related papers  (2024-11-25T14:37:24Z)
• Large Language Models for Scientific Synthesis, Inference and Explanation [56.41963802804953]
  We show how large language models can perform scientific synthesis, inference, and explanation. We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature. This approach has the further advantage that the large language model can explain the machine learning system's predictions.
  arXiv  Detail & Related papers  (2023-10-12T02:17:59Z)
• Large Language Models for Information Retrieval: A Survey [58.30439850203101]
  Information retrieval has evolved from term-based methods to its integration with advanced neural models. Recent research has sought to leverage large language models (LLMs) to improve IR systems. We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
  arXiv  Detail & Related papers  (2023-08-14T12:47:22Z)
• A Survey of Large Language Models [81.06947636926638]
  Language modeling has been widely studied for language understanding and generation in the past two decades. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
  arXiv  Detail & Related papers  (2023-03-31T17:28:46Z)
• On the cross-lingual transferability of multilingual prototypical models across NLU tasks [2.44288434255221]
  Supervised deep learning-based approaches have been applied to task-oriented dialog and have proven to be effective for limited domain and language applications. In practice, these approaches suffer from the drawbacks of domain-driven design and under-resourced languages. This article proposes to investigate the cross-lingual transferability of using synergistically few-shot learning with prototypical neural networks and multilingual Transformers-based models.
  arXiv  Detail & Related papers  (2022-07-19T09:55:04Z)
• Pre-Trained Language Models for Interactive Decision-Making [72.77825666035203]
  We describe a framework for imitation learning in which goals and observations are represented as a sequence of embeddings. We demonstrate that this framework enables effective generalization across different environments. For test tasks involving novel goals or novel scenes, initializing policies with language models improves task completion rates by 43.6%.
  arXiv  Detail & Related papers  (2022-02-03T18:55:52Z)
• Language Models are not Models of Language [0.0]
  Transfer learning has enabled large deep learning neural networks trained on the language modeling task to vastly improve performance. We argue that the term language model is misleading because deep learning models are not theoretical models of language.
  arXiv  Detail & Related papers  (2021-12-13T22:39:46Z)
• Ten Quick Tips for Deep Learning in Biology [116.78436313026478]
  Machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Deep learning has become its own subfield of machine learning. In the context of biological research, deep learning has been increasingly used to derive novel insights from high-dimensional biological data.
  arXiv  Detail & Related papers  (2021-05-29T21:02:44Z)
• Neural Language Generation: Formulation, Methods, and Evaluation [13.62873478165553]
  Recent advances in neural network-based generative modeling have reignited the hopes in having computer systems capable of seamlessly conversing with humans. High capacity deep learning models trained on large scale datasets demonstrate unparalleled abilities to learn patterns in the data even in the lack of explicit supervision signals. There is no standard way to assess the quality of text produced by these generative models, which constitutes a serious bottleneck towards the progress of the field.
  arXiv  Detail & Related papers  (2020-07-31T00:08:28Z)
• The Unstoppable Rise of Computational Linguistics in Deep Learning [17.572024590374305]
  We trace the history of neural networks applied to natural language understanding tasks. We argue that Transformer is not a sequence model but an induced-structure model.
  arXiv  Detail & Related papers  (2020-05-13T16:51:02Z)

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.