Generating Datasets with Pretrained Language Models
To obtain high-quality sentence embeddings from pretrained language models (PLMs), they must either be augmented with additional pretraining objectives or finetuned on a large set of labeled text pairs. While the latter approach typically outperforms the former, it requires great human effort to generate suitable datasets of sufficient size. In this paper, we show how PLMs can be leveraged to obtain high-quality sentence embeddings without the need for labeled data, finetuning or modifications to the pretraining objective: We utilize the generative abilities of large and high-performing PLMs to generate entire datasets of labeled text pairs from scratch, which we then use for finetuning much smaller and more efficient models. Our fully unsupervised approach outperforms strong baselines on several semantic textual similarity datasets.
Code (2)
Tasks
Semantic Textual SimilaritySentenceSentence EmbeddingsSimilar Papers 제목 키워드 기반
Augmenting Slot Values and Contexts for Spoken Language Understanding with Pretrained Models
Spoken Language Understanding (SLU) is one essential step in building a dialogue system. Due to the expensive cost of obtaining the labeled data, SLU suffers from the data scarcity problem. Therefore, in this paper, we f…
Data Augmentationslot-fillingSlot FillingSpoken Language UnderstandingGIST: Generating Image-Specific Text for Fine-grained Object Classification
Recent vision-language models outperform vision-only models on many image classification tasks. However, because of the absence of paired text/image descriptions, it remains difficult to fine-tune these models for fine-g…
ClassificationFine-Grained Image Classificationimage-classificationImage Classification+6Generative Language Model for Catalyst Discovery
Discovery of novel and promising materials is a critical challenge in the field of chemistry and material science, traditionally approached through methodologies ranging from trial-and-error to machine learning-driven in…
Language ModelingLanguage ModellingmodelvalidDiverse Rare Sample Generation with Pretrained GANs
Deep generative models are proficient in generating realistic data but struggle with producing rare samples in low density regions due to their scarcity of training datasets and the mode collapse problem. While recent me…
Density EstimationDiversityControlling Pretrained Language Generation Models by Learning to Focus
Transformer-based language models, which are pretrained on large-scale unsupervised data and then finetuned on task-specific datasets, have become the dominant paradigm for various natural language generation tasks. The …
Abstractive Text SummarizationResponse GenerationText Generation