Papers with pre-finetuning

5 papers
Enhancing Society-Undermining Disinformation Detection through Fine-Grained Sentiment Analysis Pre-Finetuning (2024.findings-eacl)

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Challenge: a new method for disinformation detection is needed to address the issue of disinformation, authors argue . a series of rigorous experiments establishes a notable connection between disinformation and fine-grained sentiment labels .
Approach: They propose a method leveraging pre-finetuning concept for efficient detection and removal of disinformation that may undermine society.
Outcome: The proposed method improves performance across languages and languages, showing promising results.
Multi-Task Pre-Finetuning of Lightweight Transformer Encoders for Text Classification and NER (2025.emnlp-industry)

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Challenge: nave multitask pre-finetuning introduces conflicting optimization signals that degrade overall performance.
Approach: They propose a framework that enables a single shared encoder backbone with modular adapters.
Outcome: The proposed framework achieves comparable performance to individual pre-finetuning while meeting practical deployment constraint.
Muppet: Massive Multi-task Representations with Pre-Finetuning (2021.emnlp-main)

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Challenge: Recent work shows gains from pre-training and fine-tuning that are multi-task . but it can be difficult to know which intermediate tasks will best transfer .
Approach: They propose a large-scale learning stage for pre-finetuning between pre-training and fine-tun.
Outcome: The proposed model improves performance on pretrained discriminators and generation models on a wide range of tasks while improving sample efficiency during fine-tuning.
Investigating the Effect of Pre-finetuning BERT Models on NLI Involving Presuppositions (2023.findings-emnlp)

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Challenge: a study of presupposition, discourse and sarcasm suggests that pre-finetuning can improve models' performance on presimplified cases.
Approach: They propose to leverage the connection between presupposition, discourse and sarcasm to improve models' performance.
Outcome: The proposed model improves on cases involving presupposition by pre-finetuning on additional tasks and datasets.
REZE: Representation Regularization for Domain-adaptive Text Embedding Pre-finetuning (2026.acl-long)

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Challenge: Recent text embedding models often introduce task-induced bias alongside domain knowledge, leading to performance degradation.
Approach: They propose a representation regularization framework that explicitly controls representation shift during embedding pre-finetuning.
Outcome: The proposed framework outperforms standard pre-finetuning and isotropy-oriented post-hoc regularization in most settings.

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