Sarah Wiegreffe

Teach Me to Explain: A Review of Datasets for Explainable NLP.

2021 · arXiv (Cornell University)

Explainable NLP (ExNLP) has increasingly focused on collecting human-annotated explanations. These explanations are used downstream in three ways: as data augmentation to improve performance on a predictive task, as a loss signal to train models …

Arguments that Alter Minds: LLM Rationales Sway Human (and LLM) Notions of Plausibility

2026

We investigate the degree to which human (and LLM) plausibility judgments of multiplechoice commonsense benchmark answers are subject to influence by (im)plausibility arguments for or against an answer, in particular, using rationales generated by LLMs.We …

Self-Refine: Iterative Refinement with Self-Feedback

2023 · arXiv (Cornell University)

Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an approach for improving initial outputs from …