Search Results for author:
Found 9 papers, 6 papers with code
Transformer Mechanisms Mimic Frontostriatal Gating Operations When Trained on Human Working Memory Tasks
Models based on the Transformer neural network architecture have seen success on a wide variety of tasks that appear to require complex "cognitive branching" -- or the ability to maintain pursuit of one goal while accomplishing others.
Characterizing Mechanisms for Factual Recall in Language Models
By scaling up or down the value vector of these heads, we can control the likelihood of using the in-context answer on new data.
Circuit Component Reuse Across Tasks in Transformer Language Models
that it is mostly reused to solve a seemingly different task: Colored Objects (Ippolito & Callison-Burch, 2023).
Language Models Implement Simple Word2Vec-style Vector Arithmetic
A primary criticism towards language models (LMs) is their inscrutability.
Does CLIP Bind Concepts? Probing Compositionality in Large Image Models
In this work, we focus on the ability of a large pretrained vision and language model (CLIP) to encode compositional concepts and to bind variables in a structure-sensitive way (e. g., differentiating ''cube behind sphere'' from ''sphere behind cube'').
ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution
Large-scale, high-quality corpora are critical for advancing research in coreference resolution.
Linearly Mapping from Image to Text Space
Prior work has shown that pretrained LMs can be taught to caption images when a vision model's parameters are optimized to encode images in the language space.
Pretraining on Interactions for Learning Grounded Affordance Representations
Lexical semantics and cognitive science point to affordances (i. e. the actions that objects support) as critical for understanding and representing nouns and verbs.
Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts
Sports broadcasters inject drama into play-by-play commentary by building team and player narratives through subjective analyses and anecdotes.
