Developmentally-plausible Working Memory Shapes a Critical Period for Language Acquisition

• URL: http://arxiv.org/abs/2502.04795v2
• Date: Mon, 17 Feb 2025 01:55:26 GMT
• Title: Developmentally-plausible Working Memory Shapes a Critical Period for Language Acquisition
• Authors: Masato Mita, Ryo Yoshida, Yohei Oseki,
• Abstract summary: Large language models possess general linguistic abilities but acquire language less efficiently than humans. This study proposes a method for integrating the developmental characteristics of working memory during the critical period.
• Score: 8.43537886261228
• License:
• Abstract: Large language models possess general linguistic abilities but acquire language less efficiently than humans. This study proposes a method for integrating the developmental characteristics of working memory during the critical period, a stage when human language acquisition is particularly efficient, into the training process of language models. The proposed method introduces a mechanism that initially constrains working memory during the early stages of training and gradually relaxes this constraint in an exponential manner as learning progresses. Targeted syntactic evaluation shows that the proposed method outperforms conventional methods without memory constraints or with static memory constraints. These findings not only provide new directions for designing data-efficient language models but also offer indirect evidence supporting the role of the developmental characteristics of working memory as the underlying mechanism of the critical period in language acquisition.

Related papers

• Developmental Predictive Coding Model for Early Infancy Mono and Bilingual Vocal Continual Learning [69.8008228833895]
  We propose a small-sized generative neural network equipped with a continual learning mechanism. Our model prioritizes interpretability and demonstrates the advantages of online learning.
  arXiv  Detail & Related papers  (2024-12-23T10:23:47Z)
• Detecting Memorization in Large Language Models [0.0]
  Large language models (LLMs) have achieved impressive results in natural language processing but are prone to memorizing portions of their training data. Traditional methods for detecting memorization rely on output probabilities or loss functions. We introduce an analytical method that precisely detects memorization by examining neuron activations within the LLM.
  arXiv  Detail & Related papers  (2024-12-02T00:17:43Z)
• Assessing Code Generation with Intermediate Languages [6.999311675957218]
  This study explores the utilization of intermediate languages, including various programming languages, natural language solutions, and pseudo-code. Our findings reveal that intermediate languages generally exhibit greater efficacy in larger models that have not yet achieved state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-07-07T15:35:41Z)
• In-Memory Learning: A Declarative Learning Framework for Large Language Models [56.62616975119192]
  We propose a novel learning framework that allows agents to align with their environment without relying on human-labeled data. This entire process transpires within the memory components and is implemented through natural language. We demonstrate the effectiveness of our framework and provide insights into this problem.
  arXiv  Detail & Related papers  (2024-03-05T08:25:11Z)
• Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models [49.39276272693035]
  Large-scale pre-trained language models have shown remarkable memorizing ability. Vanilla neural networks without pre-training have been long observed suffering from the catastrophic forgetting problem. We find that 1) Vanilla language models are forgetful; 2) Pre-training leads to retentive language models; 3) Knowledge relevance and diversification significantly influence the memory formation.
  arXiv  Detail & Related papers  (2023-05-16T03:50:38Z)
• Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change [28.106524698188675]
  Recent research has revealed that neural language models at scale suffer from poor temporal generalization capability. We propose a simple yet effective lexical-level masking strategy to post-train a converged language model.
  arXiv  Detail & Related papers  (2022-10-31T08:12:41Z)
• Training Language Models with Memory Augmentation [28.4608705738799]
  We present a novel training approach designed for training language models with memory augmentation. Our approach uses a training objective that directly takes in-batch examples as accessible memory. We demonstrate significant gains over previous memory-augmented approaches.
  arXiv  Detail & Related papers  (2022-05-25T11:37:29Z)
• Towards Lifelong Learning of Multilingual Text-To-Speech Synthesis [87.75833205560406]
  This work presents a lifelong learning approach to train a multilingual Text-To-Speech (TTS) system. It does not require pooled data from all languages altogether, and thus alleviates the storage and computation burden.
  arXiv  Detail & Related papers  (2021-10-09T07:00:38Z)
• Pre-trained Language Model Based Active Learning for Sentence Matching [18.48335957524662]
  We propose a pre-trained language model based active learning approach for sentence matching. Our approach can achieve greater accuracy with fewer labeled training instances.
  arXiv  Detail & Related papers  (2020-10-12T08:24:36Z)
• Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning [74.25168207651376]
  Fine-tuning pre-trained language models to downstream cross-lingual tasks has shown promising results. We leverage continual learning to preserve the cross-lingual ability of the pre-trained model when we fine-tune it to downstream tasks. Our methods achieve better performance than other fine-tuning baselines on the zero-shot cross-lingual part-of-speech tagging and named entity recognition tasks.
  arXiv  Detail & Related papers  (2020-04-29T14:07:18Z)
• Data Annealing for Informal Language Understanding Tasks [66.2988222278475]
  We propose a data annealing transfer learning procedure to bridge the performance gap on informal language tasks. It successfully utilizes a pre-trained model such as BERT in informal language.
  arXiv  Detail & Related papers  (2020-04-24T09:27:09Z)

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.