221 citations · 259 across the 22 of their papers we have counts for
4 papers · 1 filter
Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals
Yanai Elazar, Bhargavi Paranjape, Hao Peng +5
The inevitable appearance of spurious correlations in training datasets hurts the generalization of NLP models on unseen data. Previous work has found that datasets with paired inp…
Editing Common Sense in Transformers
Anshita Gupta, Debanjan Mondal, Akshay Krishna Sheshadri +4
Editing model parameters directly in Transformers makes updating open-source transformer-based models possible without re-training (Meng et al., 2023). However, these editing metho…
Increasing Probability Mass on Answer Choices Does Not Always Improve Accuracy
Sarah Wiegreffe, Matthew Finlayson, Oyvind Tafjord +2
When pretrained language models (LMs) are applied to discriminative tasks such as multiple-choice questions, they place probability mass on vocabulary tokens that aren't among the…
Self-Refine: Iterative Refinement with Self-Feedback
Aman Madaan, Niket Tandon, Prakhar Gupta +13
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…