Why can neural language models solve next-word prediction? A mathematical perspective

• URL: http://arxiv.org/abs/2306.17184v1
• Date: Tue, 20 Jun 2023 10:41:23 GMT
• Title: Why can neural language models solve next-word prediction? A mathematical perspective
• Authors: Vinoth Nandakumar, Peng Mi and Tongliang Liu
• Abstract summary: 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.
• Score: 53.807657273043446
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, deep learning has revolutionized the field of natural language processing, with neural language models proving to be very effective for next-word prediction. However, a rigorous theoretical explanation for their success in the context of formal language theory has not yet been developed, as it is unclear why neural language models can learn the combinatorial rules that govern the next-word prediction task. In this paper, we study a class of formal languages that can be used to model real-world examples of English sentences. We construct neural language models can solve the next-word prediction task in this context with zero error. Our proof highlights the different roles of the embedding layer and the fully connected component within the neural language model.

Related papers

• Meta predictive learning model of languages in neural circuits [2.5690340428649328]
  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.
  arXiv  Detail & Related papers  (2023-09-08T03:58:05Z)
• Word class representations spontaneously emerge in a deep neural network trained on next word prediction [7.240611820374677]
  How do humans learn language, and can the first language be learned at all? These fundamental questions are still hotly debated. In particular, we train an artificial deep neural network on predicting the next word. We find that the internal representations of nine-word input sequences cluster according to the word class of the tenth word to be predicted as output.
  arXiv  Detail & Related papers  (2023-02-15T11:02:50Z)
• Deep Learning Models to Study Sentence Comprehension in the Human Brain [0.1503974529275767]
  Recent artificial neural networks that process natural language achieve unprecedented performance in tasks requiring sentence-level understanding. We review works that compare these artificial language models with human brain activity and we assess the extent to which this approach has improved our understanding of the neural processes involved in natural language comprehension.
  arXiv  Detail & Related papers  (2023-01-16T10:31:25Z)
• 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)
• Long-range and hierarchical language predictions in brains and algorithms [82.81964713263483]
  We show that while deep language algorithms are optimized to predict adjacent words, the human brain would be tuned to make long-range and hierarchical predictions. This study strengthens predictive coding theory and suggests a critical role of long-range and hierarchical predictions in natural language processing.
  arXiv  Detail & Related papers  (2021-11-28T20:26:07Z)
• 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)
• You Can Do Better! If You Elaborate the Reason When Making Prediction [13.658942796267015]
  This paper proposes a novel neural predictive framework coupled with large pre-trained language models to make a prediction and generate its corresponding explanation simultaneously. We conducted a preliminary empirical study on Chinese medical multiple-choice question answering, English natural language inference and commonsense question answering tasks. The proposed method also achieves improved prediction accuracy on three datasets, which indicates that making predictions can benefit from generating the explanation in the decision process.
  arXiv  Detail & Related papers  (2021-03-27T14:55:19Z)
• The Neural Coding Framework for Learning Generative Models [91.0357317238509]
  We propose a novel neural generative model inspired by the theory of predictive processing in the brain. In a similar way, artificial neurons in our generative model predict what neighboring neurons will do, and adjust their parameters based on how well the predictions matched reality.
  arXiv  Detail & Related papers  (2020-12-07T01:20:38Z)
• 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.