Summary of First Train to Generate, Then Generate to Train: Unitedsynt5 For Few-shot Nli, by Sourav Banerjee et al.
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI
by Sourav Banerjee, Anush Mahajan, Ayushi Agarwal, Eishkaran Singh
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to improve Natural Language Inference (NLI) tasks by leveraging synthetic data augmentation. The current state-of-the-art model, Entailment Few-Shot Learning (EFL), achieves 93.1% accuracy on the Stanford Natural Language Inference (SNLI) dataset. However, this is limited by the dataset’s constraints. The authors present UnitedSynT5, an advanced extension of EFL that uses a T5-based generator to synthesize additional premise-hypothesis pairs. These augmented examples are cleaned and integrated into the training data, processed within the EFL framework. A GTR-T5-XL model is trained on this expanded dataset, achieving new benchmarks on SNLI, E-SNLI, and MultiNLI datasets, surpassing previous SOTA models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves Natural Language Inference (NLI) tasks by creating more training data using a machine learning algorithm. The current best model gets 93% of answers correct on one dataset. But it’s limited because the dataset is not very diverse. The authors create new examples that are similar to real sentences, and then use these examples to train an even better model. This new model gets even higher accuracy scores than the previous best models on three different datasets. |
Keywords
» Artificial intelligence » Few shot » Inference » Machine learning » Synthetic data » T5
