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Found 9 papers, 2 papers with code
F\^2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax
Despite recent advances in neural text generation, encoding the rich diversity in human language remains elusive.
A Simple Framework to Accelerate Multilingual Language Model for Monolingual Text Generation
Recent advancements in large language models have facilitated the execution of complex language tasks, not only in English but also in non-English languages.
Learning to Diversify Neural Text Generation via Degenerative Model
We then enhance the diversity of the second model by focusing on patterns that the first model fails to learn.
Empowering Sentence Encoders with Prompting and Label Retrieval for Zero-shot Text Classification
With contrastive pre-training, sentence encoders are generally optimized to locate semantically similar samples closer to each other in their embedding spaces.
Reweighting Strategy based on Synthetic Data Identification for Sentence Similarity
To analyze this, we first train a classifier that identifies machine-written sentences, and observe that the linguistic features of the sentences identified as written by a machine are significantly different from those of human-written sentences.
AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain
During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated.
Natural Attribute-based Shift Detection
Despite the impressive performance of deep networks in vision, language, and healthcare, unpredictable behaviors on samples from the distribution different than the training distribution cause severe problems in deployment.
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment
We evaluate the out-of-distribution (OOD) detection performance of self-supervised learning (SSL) techniques with a new evaluation framework.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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F^2-Softmax: Diversifying Neural Text Generation via Frequency Factorized Softmax
Despite recent advances in neural text generation, encoding the rich diversity in human language remains elusive.
