Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Pythonbased natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its tranformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robutstness analysis results are available publicly on the NL-Augmenter repository (https://github. com/GEM-benchmark/NL-Augmenter).
GPT transformers are the largest language models available, yet semantic search is dominated by BERT transformers. We present SGPT-BE and SGPT-CE for applying GPT models as Bi-Encoders or Cross-Encoders to symmetric or asymmetric search. SGPT-BE produces semantically meaningful sentence embeddings by contrastive fine-tuning of only bias tensors and a novel pooling method. A 5.8 billion parameter SGPT-BE outperforms the best available sentence embeddings by 6% setting a new state-of-the-art on BEIR. It outperforms the concurrently proposed OpenAI Embeddings of the 175B Davinci endpoint, which fine-tunes 250,000 times more parameters. SGPT-CE uses log probabilities from GPT models without any fine-tuning. A 6.1 billion parameter SGPT-CE sets an unsupervised state-of-the-art on BEIR. It beats the supervised state-of-the-art on 7 datasets, but significantly loses on other datasets. We show how this can be alleviated by adapting the prompt. SGPT-BE and SGPT-CE performance scales with model size. Yet, increased latency, storage and compute costs should be considered. Code, models and result files are freely available at https://github.com/Muennighoff/sgpt.Preprint. Under review.
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