Exploring Narrative Clustering in Large Language Models: A Layerwise Analysis of BERT

• URL: http://arxiv.org/abs/2501.08053v1
• Date: Tue, 14 Jan 2025 12:01:54 GMT
• Title: Exploring Narrative Clustering in Large Language Models: A Layerwise Analysis of BERT
• Authors: Awritrojit Banerjee, Achim Schilling, Patrick Krauss,
• Abstract summary: This study investigates the internal mechanisms of BERT, a transformer-based large language model. Using a dataset of narratives developed via GPT-4, we analyze BERT's layerwise activations to uncover patterns of localized neural processing. We reveal that BERT exhibits strong clustering based on narrative content in its later layers, with progressively compact and distinct clusters.
• Score: 0.0
• License:
• Abstract: This study investigates the internal mechanisms of BERT, a transformer-based large language model, with a focus on its ability to cluster narrative content and authorial style across its layers. Using a dataset of narratives developed via GPT-4, featuring diverse semantic content and stylistic variations, we analyze BERT's layerwise activations to uncover patterns of localized neural processing. Through dimensionality reduction techniques such as Principal Component Analysis (PCA) and Multidimensional Scaling (MDS), we reveal that BERT exhibits strong clustering based on narrative content in its later layers, with progressively compact and distinct clusters. While strong stylistic clustering might occur when narratives are rephrased into different text types (e.g., fables, sci-fi, kids' stories), minimal clustering is observed for authorial style specific to individual writers. These findings highlight BERT's prioritization of semantic content over stylistic features, offering insights into its representational capabilities and processing hierarchy. This study contributes to understanding how transformer models like BERT encode linguistic information, paving the way for future interdisciplinary research in artificial intelligence and cognitive neuroscience.

Related papers

• Author-Specific Linguistic Patterns Unveiled: A Deep Learning Study on Word Class Distributions [0.0]
  This study investigates author-specific word class distributions using part-of-speech (POS) tagging and bigram analysis. By leveraging deep neural networks, we classify literary authors based on POS tag vectors and bigram frequency matrices derived from their works.
  arXiv  Detail & Related papers  (2025-01-17T09:43:49Z)
• Analysis and Visualization of Linguistic Structures in Large Language Models: Neural Representations of Verb-Particle Constructions in BERT [0.0]
  This study investigates the internal representations of verb-particle combinations within large language models (LLMs) We analyse the representational efficacy of its layers for various verb-particle constructions such as 'agree on', 'come back', and 'give up' Results show that BERT's middle layers most effectively capture syntactic structures, with significant variability in representational accuracy across different verb categories.
  arXiv  Detail & Related papers  (2024-12-19T09:21:39Z)
• Empowering Interdisciplinary Research with BERT-Based Models: An Approach Through SciBERT-CNN with Topic Modeling [0.0]
  This paper introduces a novel approach using the SciBERT model and CNNs to systematically categorize academic abstracts. The CNN uses convolution and pooling to enhance feature extraction and reduce dimensionality.
  arXiv  Detail & Related papers  (2024-04-16T05:21:47Z)
• Text Clustering with Large Language Model Embeddings [0.0]
  The effectiveness of text clustering largely depends on the selection of textual embeddings and clustering algorithms. Recent advancements in large language models (LLMs) have the potential to enhance this task. Findings indicate that LLM embeddings are superior at capturing subtleties in structured language.
  arXiv  Detail & Related papers  (2024-03-22T11:08:48Z)
• Contextualization Distillation from Large Language Model for Knowledge Graph Completion [51.126166442122546]
  We introduce the Contextualization Distillation strategy, a plug-in-and-play approach compatible with both discriminative and generative KGC frameworks. Our method begins by instructing large language models to transform compact, structural triplets into context-rich segments. Comprehensive evaluations across diverse datasets and KGC techniques highlight the efficacy and adaptability of our approach.
  arXiv  Detail & Related papers  (2024-01-28T08:56:49Z)
• Exploiting Contextual Target Attributes for Target Sentiment Classification [53.30511968323911]
  Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word generation task. We present a new perspective of leveraging PTLM for TSC: simultaneously leveraging the merits of both language modeling and explicit target-context interactions via contextual target attributes.
  arXiv  Detail & Related papers  (2023-12-21T11:45:28Z)
• Knowledge-Aware Bayesian Deep Topic Model [50.58975785318575]
  We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling. Our proposed model efficiently integrates the prior knowledge and improves both hierarchical topic discovery and document representation.
  arXiv  Detail & Related papers  (2022-09-20T09:16:05Z)
• Generating More Pertinent Captions by Leveraging Semantics and Style on Multi-Source Datasets [56.018551958004814]
  This paper addresses the task of generating fluent descriptions by training on a non-uniform combination of data sources. Large-scale datasets with noisy image-text pairs provide a sub-optimal source of supervision. We propose to leverage and separate semantics and descriptive style through the incorporation of a style token and keywords extracted through a retrieval component.
  arXiv  Detail & Related papers  (2021-11-24T19:00:05Z)
• Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation [49.89831914386982]
  We propose a unified pre-trained language model (PLM) for all forms of text, including unstructured text, semi-structured text, and well-structured text. Our approach outperforms the pre-training of plain text using only 1/4 of the data.
  arXiv  Detail & Related papers  (2021-09-02T16:05:24Z)
• Combining Pre-trained Word Embeddings and Linguistic Features for Sequential Metaphor Identification [12.750941606061877]
  We tackle the problem of identifying metaphors in text, treated as a sequence tagging task. The pre-trained word embeddings GloVe, ELMo and BERT have individually shown good performance on sequential metaphor identification. We show that leveraging GloVe, ELMo and feature-based BERT can significantly outperform any single word embedding method and the combination of the two embeddings.
  arXiv  Detail & Related papers  (2021-04-07T17:43:05Z)
• Temporal Embeddings and Transformer Models for Narrative Text Understanding [72.88083067388155]
  We present two approaches to narrative text understanding for character relationship modelling. The temporal evolution of these relations is described by dynamic word embeddings, that are designed to learn semantic changes over time. A supervised learning approach based on the state-of-the-art transformer model BERT is used instead to detect static relations between characters.
  arXiv  Detail & Related papers  (2020-03-19T14:23:12Z)

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.