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Found 6 papers, 5 papers with code
LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues
Spurred by recent advances in Large Language Models (LLMs), virtual assistants are poised to take a leap forward in terms of their dialogue capabilities.
Distilling Robustness into Natural Language Inference Models with Domain-Targeted Augmentation
Knowledge distillation optimises a smaller student model to behave similarly to a larger teacher model, retaining some of the performance benefits.
Atomic Inference for NLI with Generated Facts as Atoms
With recent advances, neural models can achieve human-level performance on various natural language tasks.
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.
