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Found 6 papers, 4 papers with code
LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues
Moreover, creating high quality dialogue data has until now required considerable human input, limiting both the scale of these datasets and the ability to rapidly bootstrap data for a new target domain.
Logical Reasoning for Natural Language Inference Using Generated Facts as Atoms
We apply our method to the highly challenging ANLI dataset, where our framework improves the performance of both a DeBERTa-base and BERT baseline.
Improving Robustness in Knowledge Distillation Using Domain-Targeted Data Augmentation
DMU is complementary to the domain-targeted augmentation, and substantially improves performance on SNLI-hard.
Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models
We can further improve model performance and span-level decisions by using the e-SNLI explanations during training.
Supervising Model Attention with Human Explanations for Robust Natural Language Inference
Natural Language Inference (NLI) models are known to learn from biases and artefacts within their training data, impacting how well they generalise to other unseen datasets.
Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
Natural Language Inference (NLI) datasets contain annotation artefacts resulting in spurious correlations between the natural language utterances and their respective entailment classes.
