27 citations · 50 across the 7 of their papers we have counts for
6 papers · 1 filter
How Abstract Is Linguistic Generalization in Large Language Models? Experiments with Argument Structure
Michael Wilson, Jackson Petty, Robert Frank
Language models are typically evaluated on their success at predicting the distribution of specific words in specific contexts. Yet linguistic knowledge also encodes relationships…
In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax
Aaron Mueller, Albert Webson, Jackson Petty +1
In-context learning (ICL) is now a common method for teaching large language models (LLMs) new tasks: given labeled examples in the input context, the LLM learns to perform the tas…
The Impact of Depth on Compositional Generalization in Transformer Language Models
Jackson Petty, Sjoerd van Steenkiste, Ishita Dasgupta +3
To process novel sentences, language models (LMs) must generalize compositionally -- combine familiar elements in new ways. What aspects of a model's structure promote compositiona…
Do Language Models Learn Position-Role Mappings?
Jackson Petty, Michael Wilson, Robert Frank
How is knowledge of position-role mappings in natural language learned? We explore this question in a computational setting, testing whether a variety of well-performing pertained…
Transformers Generalize Linearly
Jackson Petty, Robert Frank
Natural language exhibits patterns of hierarchically governed dependencies, in which relations between words are sensitive to syntactic structure rather than linear ordering. While…
Sequence-to-Sequence Networks Learn the Meaning of Reflexive Anaphora
Robert Frank, Jackson Petty
Reflexive anaphora present a challenge for semantic interpretation: their meaning varies depending on context in a way that appears to require abstract variables. Past work has rai…