Meta predictive learning model of languages in neural circuits

• URL: http://arxiv.org/abs/2309.04106v2
• Date: Mon, 9 Oct 2023 08:09:17 GMT
• Title: Meta predictive learning model of languages in neural circuits
• Authors: Chan Li and Junbin Qiu and Haiping Huang
• Abstract summary: We propose a mean-field learning model within the predictive coding framework. Our model reveals that most of the connections become deterministic after learning. Our model provides a starting point to investigate the connection among brain computation, next-token prediction and general intelligence.
• Score: 2.5690340428649328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models based on self-attention mechanisms have achieved astonishing performances not only in natural language itself, but also in a variety of tasks of different nature. However, regarding processing language, our human brain may not operate using the same principle. Then, a debate is established on the connection between brain computation and artificial self-supervision adopted in large language models. One of most influential hypothesis in brain computation is the predictive coding framework, which proposes to minimize the prediction error by local learning. However, the role of predictive coding and the associated credit assignment in language processing remains unknown. Here, we propose a mean-field learning model within the predictive coding framework, assuming that the synaptic weight of each connection follows a spike and slab distribution, and only the distribution, rather than specific weights, is trained. This meta predictive learning is successfully validated on classifying handwritten digits where pixels are input to the network in sequence, and moreover on the toy and real language corpus. Our model reveals that most of the connections become deterministic after learning, while the output connections have a higher level of variability. The performance of the resulting network ensemble changes continuously with data load, further improving with more training data, in analogy with the emergent behavior of large language models. Therefore, our model provides a starting point to investigate the connection among brain computation, next-token prediction and general intelligence.

Related papers

• Training Neural Networks as Recognizers of Formal Languages [87.06906286950438]
  Formal language theory pertains specifically to recognizers. It is common to instead use proxy tasks that are similar in only an informal sense. We correct this mismatch by training and evaluating neural networks directly as binary classifiers of strings.
  arXiv  Detail & Related papers  (2024-11-11T16:33:25Z)
• Brain-Like Language Processing via a Shallow Untrained Multihead Attention Network [16.317199232071232]
  Large Language Models (LLMs) have been shown to be effective models of the human language system. In this work, we investigate the key architectural components driving the surprising alignment of untrained models.
  arXiv  Detail & Related papers  (2024-06-21T12:54:03Z)
• In-Context Language Learning: Architectures and Algorithms [73.93205821154605]
  We study ICL through the lens of a new family of model problems we term in context language learning (ICLL) We evaluate a diverse set of neural sequence models on regular ICLL tasks.
  arXiv  Detail & Related papers  (2024-01-23T18:59:21Z)
• Causal Graph in Language Model Rediscovers Cortical Hierarchy in Human Narrative Processing [0.0]
  Previous studies have demonstrated that the features of language models can be mapped to fMRI brain activity. This raises the question: is there a commonality between information processing in language models and the human brain? To estimate information flow patterns in a language model, we examined the causal relationships between different layers.
  arXiv  Detail & Related papers  (2023-11-17T10:09:12Z)
• Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distribution [4.01799362940916]
  We present a setup for training, evaluating and interpreting neural language models, that uses artificial, language-like data. The data is generated using a massive probabilistic grammar, that is itself derived from a large natural language corpus. With access to the underlying true source, our results show striking differences and outcomes in learning dynamics between different classes of words.
  arXiv  Detail & Related papers  (2023-10-23T12:03:01Z)
• Why can neural language models solve next-word prediction? A mathematical perspective [53.807657273043446]
  We study a class of formal languages that can be used to model real-world examples of English sentences. Our proof highlights the different roles of the embedding layer and the fully connected component within the neural language model.
  arXiv  Detail & Related papers  (2023-06-20T10:41:23Z)
• Dependency-based Mixture Language Models [53.152011258252315]
  We introduce the Dependency-based Mixture Language Models. In detail, we first train neural language models with a novel dependency modeling objective. We then formulate the next-token probability by mixing the previous dependency modeling probability distributions with self-attention.
  arXiv  Detail & Related papers  (2022-03-19T06:28:30Z)
• 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)
• Towards Zero-shot Language Modeling [90.80124496312274]
  We construct a neural model that is inductively biased towards learning human languages. We infer this distribution from a sample of typologically diverse training languages. We harness additional language-specific side information as distant supervision for held-out languages.
  arXiv  Detail & Related papers  (2021-08-06T23:49:18Z)
• Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humans [75.15855405318855]
  We studied whether a modern artificial neural network trained with "deep learning" methods mimics a central aspect of human sentence processing. Although the network was solely trained to predict the next word in a large corpus, analysis showed the emergence of specialized units that successfully handled local and long-distance syntactic agreement. We tested the model's predictions in a behavioral experiment where humans detected violations in number agreement in sentences with systematic variations in the singular/plural status of multiple nouns.
  arXiv  Detail & Related papers  (2020-06-19T12:00:05Z)

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.