Analyzing Individual Neurons in Pre-trained Language Models

• URL: http://arxiv.org/abs/2010.02695v1
• Date: Tue, 6 Oct 2020 13:17:38 GMT
• Title: Analyzing Individual Neurons in Pre-trained Language Models
• Authors: Nadir Durrani and Hassan Sajjad and Fahim Dalvi and Yonatan Belinkov
• Abstract summary: We find small subsets of neurons to predict linguistic tasks, with lower level tasks localized in fewer neurons, compared to higher level task of predicting syntax. For example, we found neurons in XLNet to be more localized and disjoint when predicting properties compared to BERT and others, where they are more distributed and coupled.
• Score: 41.07850306314594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While a lot of analysis has been carried to demonstrate linguistic knowledge captured by the representations learned within deep NLP models, very little attention has been paid towards individual neurons.We carry outa neuron-level analysis using core linguistic tasks of predicting morphology, syntax and semantics, on pre-trained language models, with questions like: i) do individual neurons in pre-trained models capture linguistic information? ii) which parts of the network learn more about certain linguistic phenomena? iii) how distributed or focused is the information? and iv) how do various architectures differ in learning these properties? We found small subsets of neurons to predict linguistic tasks, with lower level tasks (such as morphology) localized in fewer neurons, compared to higher level task of predicting syntax. Our study also reveals interesting cross architectural comparisons. For example, we found neurons in XLNet to be more localized and disjoint when predicting properties compared to BERT and others, where they are more distributed and coupled.

Related papers

• Analysis of Argument Structure Constructions in a Deep Recurrent Language Model [0.0]
  We explore the representation and processing of Argument Structure Constructions (ASCs) in a recurrent neural language model. Our results show that sentence representations form distinct clusters corresponding to the four ASCs across all hidden layers. This indicates that even a relatively simple, brain-constrained recurrent neural network can effectively differentiate between various construction types.
  arXiv  Detail & Related papers  (2024-08-06T09:27:41Z)
• Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMs [70.3132264719438]
  We aim to fill the research gap by examining how neuron activation is shared across tasks and languages. We classify neurons into four distinct categories based on their responses to a specific input across different languages. Our analysis reveals the following insights: (i) the patterns of neuron sharing are significantly affected by the characteristics of tasks and examples; (ii) neuron sharing does not fully correspond with language similarity; (iii) shared neurons play a vital role in generating responses, especially those shared across all languages.
  arXiv  Detail & Related papers  (2024-06-13T16:04:11Z)
• Identification of Knowledge Neurons in Protein Language Models [0.0]
  We identify and characterizing knowledge neurons, components that express understanding of key information. We show that there is a high density of knowledge neurons in the key vector prediction networks of self-attention modules. In the future, the types of knowledge captured by each neuron could be characterized.
  arXiv  Detail & Related papers  (2023-12-17T17:23:43Z)
• Discovering Salient Neurons in Deep NLP Models [31.18937787704794]
  We present a technique called as Linguistic Correlation Analysis to extract salient neurons in the model. Our data-driven, quantitative analysis illuminates interesting findings. Our code is publicly available as part of the NeuroX toolkit.
  arXiv  Detail & Related papers  (2022-06-27T13:31:49Z)
• Same Neurons, Different Languages: Probing Morphosyntax in Multilingual Pre-trained Models [84.86942006830772]
  We conjecture that multilingual pre-trained models can derive language-universal abstractions about grammar. We conduct the first large-scale empirical study over 43 languages and 14 morphosyntactic categories with a state-of-the-art neuron-level probe.
  arXiv  Detail & Related papers  (2022-05-04T12:22:31Z)
• 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)
• What do End-to-End Speech Models Learn about Speaker, Language and Channel Information? A Layer-wise and Neuron-level Analysis [16.850888973106706]
  We conduct a post-hoc functional interpretability analysis of pretrained speech models using the probing framework. We analyze utterance-level representations of speech models trained for various tasks such as speaker recognition and dialect identification. Our results reveal several novel findings, including: i) channel and gender information are distributed across the network, ii) the information is redundantly available in neurons with respect to a task, and iv) complex properties such as dialectal information are encoded only in the task-oriented pretrained network.
  arXiv  Detail & Related papers  (2021-07-01T13:32:55Z)
• Does injecting linguistic structure into language models lead to better alignment with brain recordings? [13.880819301385854]
  We evaluate whether language models align better with brain recordings if their attention is biased by annotations from syntactic or semantic formalisms. Our proposed approach enables the evaluation of more targeted hypotheses about the composition of meaning in the brain.
  arXiv  Detail & Related papers  (2021-01-29T14:42:02Z)
• Compositional Explanations of Neurons [52.71742655312625]
  We describe a procedure for explaining neurons in deep representations by identifying compositional logical concepts. We use this procedure to answer several questions on interpretability in models for vision and natural language processing.
  arXiv  Detail & Related papers  (2020-06-24T20:37:05Z)
• 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.