6 citations · 20 across the 21 of their papers we have counts for
12 papers · 1 filter
Interpreting Brain Responses to Language with Sparse Features from Language Models
Michael A. Lepori, Kendrick Kay, Greta Tuckute
A central goal of cognitive neuroscience is to characterize the features that are represented by human language cortex. Artificial language models (LMs) have emerged as a powerful…
Language Models Struggle to Use Representations Learned In-Context
Michael A. Lepori, Tal Linzen, Ann Yuan +1
Though large language models (LLMs) have enabled great success across a wide variety of tasks, they still appear to fall short of one of the loftier goals of artificial intelligenc…
Is This Just Fantasy? Language Model Representations Reflect Human Judgments of Event Plausibility
Michael A. Lepori, Jennifer Hu, Ishita Dasgupta +3
Language models (LMs) are used for a diverse range of tasks, from question answering to writing fantastical stories. In order to reliably accomplish these tasks, LMs must be able t…
Are LLMs Models of Distributional Semantics? A Case Study on Quantifiers
Zhang Enyan, Zewei Wang, Michael A. Lepori +2
Distributional semantics is the linguistic theory that a word's meaning can be derived from its distribution in natural language (i.e., its use). Language models are commonly viewe…
Racing Thoughts: Explaining Contextualization Errors in Large Language Models
Michael A. Lepori, Michael C. Mozer, Asma Ghandeharioun
The profound success of transformer-based language models can largely be attributed to their ability to integrate relevant contextual information from an input sequence in order to…
Who's asking? User personas and the mechanics of latent misalignment
Asma Ghandeharioun, Ann Yuan, Marius Guerard +3
Despite investments in improving model safety, studies show that misaligned capabilities remain latent in safety-tuned models. In this work, we shed light on the mechanics of this…
Dual Process Learning: Controlling Use of In-Context vs. In-Weights Strategies with Weight Forgetting
Suraj Anand, Michael A. Lepori, Jack Merullo +1
Language models have the ability to perform in-context learning (ICL), allowing them to flexibly adapt their behavior based on context. This contrasts with in-weights learning (IWL…
Uncovering Intermediate Variables in Transformers using Circuit Probing
Michael A. Lepori, Thomas Serre, Ellie Pavlick
Neural network models have achieved high performance on a wide variety of complex tasks, but the algorithms that they implement are notoriously difficult to interpret. It is often…
Break It Down: Evidence for Structural Compositionality in Neural Networks
Michael A. Lepori, Thomas Serre, Ellie Pavlick
Though modern neural networks have achieved impressive performance in both vision and language tasks, we know little about the functions that they implement. One possibility is tha…
Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis
Michael A. Lepori
We present a new approach for detecting human-like social biases in word embeddings using representational similarity analysis. Specifically, we probe contextualized and non-contex…
Picking BERT's Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis
Michael A. Lepori, R. Thomas McCoy
As the name implies, contextualized representations of language are typically motivated by their ability to encode context. Which aspects of context are captured by such representa…
Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs
Michael A. Lepori, Tal Linzen, R. Thomas McCoy
Sequence-based neural networks show significant sensitivity to syntactic structure, but they still perform less well on syntactic tasks than tree-based networks. Such tree-based ne…