Search Results for author:
Found 14 papers, 7 papers with code
Language Modeling with Editable External Knowledge
This paper introduces ERASE, which instead improves model behavior when new documents are acquired, by incrementally deleting or rewriting other entries in the knowledge base each time a document is added.
Learning Phonotactics from Linguistic Informants
We propose an interactive approach to language learning that utilizes linguistic acceptability judgments from an informant (a competent language user) to learn a grammar.
Toward In-Context Teaching: Adapting Examples to Students' Misconceptions
AdapT has two components: (1) a collection of simulated Bayesian student models that can be used for evaluation of automated teaching methods; (2) a platform for evaluation with human students, to characterize the real-world effectiveness of these methods.
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks
The impressive performance of recent language models across a wide range of tasks suggests that they possess a degree of abstract reasoning skills.
ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews
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.
Inverse Scaling: When Bigger Isn't Better
Here, we present evidence for the claim that LMs may show inverse scaling, or worse task performance with increased scale, e. g., due to flaws in the training objective and data.
CREST: A Joint Framework for Rationalization and Counterfactual Text Generation
Selective rationales and counterfactual examples have emerged as two effective, complementary classes of interpretability methods for analyzing and training NLP models.
Does Self-Rationalization Improve Robustness to Spurious Correlations?
Specifically, we evaluate how training self-rationalization models with free-text rationales affects robustness to spurious correlations in fine-tuned encoder-decoder and decoder-only models of six different sizes.
Tailor: Generating and Perturbing Text with Semantic Controls
We craft a set of operations to modify the control codes, which in turn steer generation towards targeted attributes.
Competency Problems: On Finding and Removing Artifacts in Language Data
In this work we argue that for complex language understanding tasks, all simple feature correlations are spurious, and we formalize this notion into a class of problems which we call competency problems.
Explaining NLP Models via Minimal Contrastive Editing (MiCE)
Humans have been shown to give contrastive explanations, which explain why an observed event happened rather than some other counterfactual event (the contrast case).
Learning Models for Actionable Recourse
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 generating counterfactual explanations.
How well do NLI models capture verb veridicality?
In natural language inference (NLI), contexts are considered veridical if they allow us to infer that their underlying propositions make true claims about the real world.
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
Our results show that pretraining on language modeling performs the best on average across our probing tasks, supporting its widespread use for pretraining state-of-the-art NLP models, and CCG supertagging and NLI pretraining perform comparably.
