24 citations · 48 across the 10 of their papers we have counts for
11 papers
Toward In-Context Teaching: Adapting Examples to Students' Misconceptions
Alexis Ross, Jacob Andreas
When a teacher provides examples for a student to study, these examples must be informative, enabling a student to progress from their current state toward a target concept or skil…
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks
Zhaofeng Wu, Linlu Qiu, Alexis Ross +6
The impressive performance of recent language models across a wide range of tasks suggests that they possess a degree of abstract reasoning skills. Are these skills general and tra…
ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews
Mike D'Arcy, Alexis Ross, Erin Bransom +4
We introduce the task of automatically revising scientific papers based on peer feedback and release ARIES, a dataset of review comments and their corresponding paper edits. The da…
Inverse Scaling: When Bigger Isn't Better
Ian R. McKenzie, Alexander Lyzhov, Michael Pieler +24
Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, w…
CREST: A Joint Framework for Rationalization and Counterfactual Text Generation
Marcos Treviso, Alexis Ross, Nuno M. Guerreiro +1
Selective rationales and counterfactual examples have emerged as two effective, complementary classes of interpretability methods for analyzing and training NLP models. However, pr…
Does Self-Rationalization Improve Robustness to Spurious Correlations?
Alexis Ross, Matthew E. Peters, Ana Marasović
Rationalization is fundamental to human reasoning and learning. NLP models trained to produce rationales along with predictions, called self-rationalization models, have been inves…
Tailor: Generating and Perturbing Text with Semantic Controls
Alexis Ross, Tongshuang Wu, Hao Peng +2
Controlled text perturbation is useful for evaluating and improving model generalizability. However, current techniques rely on training a model for every target perturbation, whic…
Competency Problems: On Finding and Removing Artifacts in Language Data
Matt Gardner, William Merrill, Jesse Dodge +4
Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have "spurio…
Explaining NLP Models via Minimal Contrastive Editing (MiCE)
Alexis Ross, Ana Marasović, Matthew E. Peters
Humans have been shown to give contrastive explanations, which explain why an observed event happened rather than some other counterfactual event (the contrast case). Despite the i…
Learning Models for Actionable Recourse
Alexis Ross, Himabindu Lakkaraju, Osbert Bastani
As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for gen…
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
Najoung Kim, Roma Patel, Adam Poliak +9
We introduce a set of nine challenge tasks that test for the understanding of function words. These tasks are created by structurally mutating sentences from existing datasets to t…