Six Datasets
Related Papers
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
AbstractFine-tuned pre-trained language models can suffer from severe miscalibration for both in-distribution and out-of-distribution (OOD) data due to over-parameterization. To mitigate this issue, we propose a regularized fine-tuning method. Our …...
Combining Shallow and Deep Representations for Text-Pair Classification
AbstractText-pair classification is the task of determining the class relationship between two sentences. It is embedded in several tasks such as paraphrase identification and duplicate question detection. Contemporary methods use …...
Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models
AbstractRecent works have shown that powerful pre-trained language models (PLM) can be fooled by small perturbations or intentional attacks. To solve this issue, various data augmentation techniques are proposed to …...
Prompt-based Distribution Alignment for Domain Generalization in Text Classification
AbstractPrompt-based learning (a.k.a. prompting) achieves high performance by bridging the gap between the objectives of language modeling and downstream tasks. Domain generalization ability can be improved by prompting since classification …...
Striking a Balance: Alleviating Inconsistency in Pre-trained Models for Symmetric Classification Tasks
AbstractWhile fine-tuning pre-trained models for downstream classification is the conventional paradigm in NLP, often task-specific nuances may not get captured in the resultant models. Specifically, for tasks that take two …...
Multi-Source Test-Time Adaptation as Dueling Bandits for Extractive Question Answering
AbstractIn this work, we study multi-source test-time model adaptation from user feedback, where K distinct models are established for adaptation. To allow efficient adaptation, we cast the problem as a …...