Papers › First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI
First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI
Sourav Banerjee, Anush Mahajan, Ayushi Agarwal, Eishkaran Singh
Natural Language Inference (NLI) tasks require identifying the relationship between sentence pairs, typically classified as entailment, contradiction, or neutrality. While the current state-of-the-art (SOTA) model, Entailment Few-Shot Learning (EFL), achieves a 93.1% accuracy on the Stanford Natural Language Inference (SNLI) dataset, further advancements are constrained by the dataset's limitations. To address this, we propose a novel approach leveraging synthetic data augmentation to enhance dataset diversity and complexity. We present UnitedSynT5, an advanced extension of EFL that leverages a T5-based generator to synthesize additional premise-hypothesis pairs, which are rigorously cleaned and integrated into the training data. These augmented examples are processed within the EFL framework, embedding labels directly into hypotheses for consistency. We train a GTR-T5-XL model on this expanded dataset, achieving a new benchmark of 94.7% accuracy on the SNLI dataset, 94.0% accuracy on the E-SNLI dataset, and 92.6% accuracy on the MultiNLI dataset, surpassing the previous SOTA models. This research demonstrates the potential of synthetic data augmentation in improving NLI models, offering a path forward for further advancements in natural language understanding tasks.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Natural Language Inference | MultiNLI | UnitedSynT5 (3B) | Matched | 92.6 | #2 of 67 | Archive leaderboard | report |
| Natural Language Inference | MultiNLI | UnitedSynT5 (335M) | Matched | 89.8 | #14 of 67 | Archive leaderboard | report |
| Natural Language Inference | SNLI | UnitedSynT5 (3B) | % Test Accuracy | 94.7 | #1 of 98 | Archive leaderboard | report |
| Natural Language Inference | SNLI | UnitedSynT5 (335M) | % Test Accuracy | 93.5 | #2 of 98 | Archive leaderboard | report |
| Natural Language Inference | e-SNLI | UnitedSynT5 (3B) | Accuracy | 94.0 | #2 of 3 | Archive leaderboard | report |
| Natural Language Inference | e-SNLI | UnitedSynT5 (335M) | Accuracy | 89.8 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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