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Paper Citation Record · LEDGER

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study

As of 13 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2412.00098.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.00098 v2

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measured 34 of 34 reference resolution

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One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

34 of 34 outbound references displayed

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Outbound references

Observation 3d275193-437c-4ddd-aef2-af9e566450ae · outbound

This paper cites Exploring small language models with prompt-learning paradigm for efficient domain-specific text classifica- tion,.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Exploring small language models with prompt-learning paradigm for efficient domain-specific text classifica- tion,

Reference 1

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This paper cites Multilabel text classification with label-dependent representation,.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Multilabel text classification with label-dependent representation,

Reference 2

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This paper cites Survey of fake news datasets and detection methods in european and asian languages,.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Survey of fake news datasets and detection methods in european and asian languages,

Reference 3

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This paper cites Novel deep learning approach for sci- entific literature classification,.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Novel deep learning approach for sci- entific literature classification,

Reference 4

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This paper cites Benchmarking for biomedical natural language processing tasks with a domain specific albert,.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Benchmarking for biomedical natural language processing tasks with a domain specific albert,

Reference 5

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This paper cites A brief survey of text classification methods,.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study A brief survey of text classification methods,

Reference 6

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This paper cites A survey of text classification with transformers: How wide? how large? how long? how accurate? how expensive? how safe?,.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study A survey of text classification with transformers: How wide? how large? how long? how accurate? how expensive? how safe?,

Reference 7

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This paper cites Survey of pre-trained models for natural language processing,.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Survey of pre-trained models for natural language processing,

Reference 8

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This paper cites Text Classification via Large Language Models.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Text Classification via Large Language Models

Reference 9

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study A Survey of Large Language Models

Reference 10

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This paper cites Cross-domain limi- tations of neural models on biomedical relation classification,.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Cross-domain limi- tations of neural models on biomedical relation classification,

Reference 11

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This paper cites FinBERT: Financial Sentiment Analysis with Pre-trained Language Models.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Reference 12

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This paper cites Sentiment analysis with neural models for hungarian,.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Sentiment analysis with neural models for hungarian,

Reference 13

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Topic modeling: a comprehensive review,

Reference 14

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Inte- grating text classification into topic discovery using semantic embedding models,

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This paper cites Attention is all you need,.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Attention is all you need,

Reference 16

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 17

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Language model behavior: A compre- hensive survey,

Reference 18

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This paper cites Large language models for text classification: From zero-shot learning to fine-tuning,.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Large language models for text classification: From zero-shot learning to fine-tuning,

Reference 19

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study SciBERT: A Pretrained Language Model for Scientific Text

Reference 20

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Biobert: a pre-trained biomedical language representation model for biomedical text mining,

Reference 21

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Transfer learning in biomedical natural lan- guage processing: An evaluation of bert and elmo on ten benchmarking datasets,

Reference 22

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Finbert: A pre-trained financial language representation model for financial text mining,

Reference 23

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This paper cites Structured information extraction from complex scientific text with fine-tuned large language models.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Structured information extraction from complex scientific text with fine-tuned large language models

Reference 24

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Matscibert: A materials domain language model for text mining and information extraction,

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Medibiodeberta: Biomedical lan- guage model with continuous learning and intermediate fine-tuning,

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Hdltex: Hierarchical deep learning for text classification,

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study A lightweight biomedical named entity recognition with pre-trained model,

Reference 28

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Named entity recognition using transfer learning with the fusion of pre-trained scibert language model and bi-directional long short term memory,

Reference 29

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This paper cites A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers.

Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers

Reference 30

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study S2ORC: The Semantic Scholar Open Research Corpus

Reference 31

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study A Survey of Active Learning for Text Classification using Deep Neural Networks

Reference 32

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Recurrent convolutional neural networks for text classification,

Reference 33

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Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks

Reference 34

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Pith citing papers

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Efficient Scientific Full Text Classification: The Case of EICAT Impact Assessments cites this paper.

Efficient Scientific Full Text Classification: The Case of EICAT Impact Assessments Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study

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