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SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations.

, , and . CoRR, (2023)

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Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods., , , , and . CoRR, (2019)SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations., , and . CoRR, (2023)Identifying Linear Relational Concepts in Large Language Models., , and . CoRR, (2023)A Surprisingly Robust Trick for the Winograd Schema Challenge., , , , and . ACL (1), page 4837-4842. Association for Computational Linguistics, (2019)Logical Reasoning for Natural Language Inference Using Generated Facts as Atoms., , , , and . CoRR, (2023)The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets., , , , and . CoRR, (2020)Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations., , , and . CoRR, (2021)Towards Explainable and Trustworthy Autonomous Physical Systems., , , , , and . CHI Extended Abstracts, page 88:1-88:3. ACM, (2021)Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations., , , , and . ACL, page 4157-4165. Association for Computational Linguistics, (2020)Using Natural Language Explanations to Improve Robustness of In-context Learning for Natural Language Inference., , , , and . CoRR, (2023)