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Found 6 papers, 1 papers with code
Prompting for explanations improves Adversarial NLI. Is this true? {Yes} it is {true} because {it weakens superficial cues}
Explanation prompts ask language models to not only assign a particular label to a giveninput, such as true, entailment, or contradiction in the case of natural language inference but also to generate a free-text explanation that supports this label.
Ranked #1 on
Natural Language Inference
on ANLI test
Are Prompt-based Models Clueless?
However, models with a task-specific head require a lot of training data, making them susceptible to learning and exploiting dataset-specific superficial cues that do not generalize to other datasets.
COPA-SSE: Semi-structured Explanations for Commonsense Reasoning
We present Semi-Structured Explanations for COPA (COPA-SSE), a new crowdsourced dataset of 9, 747 semi-structured, English common sense explanations for Choice of Plausible Alternatives (COPA) questions.
Learning to Learn to be Right for the Right Reasons
Here, we propose to explicitly learn a model that does well on both the easy test set with superficial cues and hard test set without superficial cues.
Improving Evidence Detection by Leveraging Warrants
Recognizing the implicit link between a claim and a piece of evidence (i. e. warrant) is the key to improving the performance of evidence detection.
When Choosing Plausible Alternatives, Clever Hans can be Clever
Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA.
