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I Learn Better If You Speak My Language: Enhancing Large Language Model Fine-Tuning with Style-Aligned Response Adjustments
The potential for overfitting on a limited number of examples can negatively impact the model's ability to generalize and retain its original skills.
You Can Generate It Again: Data-to-text Generation with Verification and Correction Prompting
In this paper, we propose a novel approach that goes beyond traditional one-shot generation methods by introducing a multi-step process consisting of generation, verification, and correction stages.
Out-of-Distribution Generalization in Text Classification: Past, Present, and Future
Machine learning (ML) systems in natural language processing (NLP) face significant challenges in generalizing to out-of-distribution (OOD) data, where the test distribution differs from the training data distribution.
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