{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/first-train-to-generate-then-generate-to","title":"First Train to Generate, then Generate to Train: UnitedSynT5 for Few-Shot NLI","arxiv_id":"2412.09263","date":"2024-12-12","proceeding":null,"authors":["Sourav Banerjee","Anush Mahajan","Ayushi Agarwal","Eishkaran Singh"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2412.09263v2","url_pdf":"https://arxiv.org/pdf/2412.09263v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"few-shot-nli","task_name":"Few-Shot NLI"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/natural-language-inference-on-multinli","task":"Natural Language Inference","dataset":"MultiNLI","model":"UnitedSynT5 (3B)","rank_in_archive_order":2,"of":67,"metrics":{"Matched":"92.6"},"uses_additional_data":true},{"leaderboard":"/sota/natural-language-inference-on-multinli","task":"Natural Language Inference","dataset":"MultiNLI","model":"UnitedSynT5 (335M)","rank_in_archive_order":14,"of":67,"metrics":{"Matched":"89.8"},"uses_additional_data":true},{"leaderboard":"/sota/natural-language-inference-on-snli","task":"Natural Language Inference","dataset":"SNLI","model":"UnitedSynT5 (3B)","rank_in_archive_order":1,"of":98,"metrics":{"% Test Accuracy":"94.7"},"uses_additional_data":true},{"leaderboard":"/sota/natural-language-inference-on-snli","task":"Natural Language Inference","dataset":"SNLI","model":"UnitedSynT5 (335M)","rank_in_archive_order":2,"of":98,"metrics":{"% Test Accuracy":"93.5"},"uses_additional_data":true},{"leaderboard":"/sota/natural-language-inference-on-e-snli","task":"Natural Language Inference","dataset":"e-SNLI","model":"UnitedSynT5 (3B)","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"94.0"},"uses_additional_data":true},{"leaderboard":"/sota/natural-language-inference-on-e-snli","task":"Natural Language Inference","dataset":"e-SNLI","model":"UnitedSynT5 (335M)","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"89.8"},"uses_additional_data":true}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}