565 citations · 1.1k across the 53 of their papers we have counts for
39 papers · 1 filter
Slow to See, Slow to Suppress: Understanding the Effects of Modality in Context-Memory Conflicts
Athulith Paraselli, Etha Tianze Hua, Ellie Pavlick
We investigate how vision-language models (VLMs) handle context-memory conflicts; that is, situations in which the model is given information in context that differs from what was…
Transferring Linear Features Across Language Models With Model Stitching
Alan Chen, Jack Merullo, Alessandro Stolfo +1
In this work, we demonstrate that affine mappings between residual streams of language models is a cheap way to effectively transfer represented features between models. We apply 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…
The Same But Different: Structural Similarities and Differences in Multilingual Language Modeling
Ruochen Zhang, Qinan Yu, Matianyu Zang +2
We employ new tools from mechanistic interpretability in order to ask whether the internal structure of large language models (LLMs) shows correspondence to the linguistic structur…
$100K or 100 Days: Trade-offs when Pre-Training with Academic Resources
Apoorv Khandelwal, Tian Yun, Nihal V. Nayak +4
Pre-training is notoriously compute-intensive and academic researchers are notoriously under-resourced. It is, therefore, commonly assumed that academics can't pre-train models. In…
Talking Heads: Understanding Inter-layer Communication in Transformer Language Models
Jack Merullo, Carsten Eickhoff, Ellie Pavlick
Although it is known that transformer language models (LMs) pass features from early layers to later layers, it is not well understood how this information is represented and route…
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…
Characterizing Mechanisms for Factual Recall in Language Models
Qinan Yu, Jack Merullo, Ellie Pavlick
Language Models (LMs) often must integrate facts they memorized in pretraining with new information that appears in a given context. These two sources can disagree, causing competi…
Circuit Component Reuse Across Tasks in Transformer Language Models
Jack Merullo, Carsten Eickhoff, Ellie Pavlick
Recent work in mechanistic interpretability has shown that behaviors in language models can be successfully reverse-engineered through circuit analysis. A common criticism, however…
Testing Causal Models of Word Meaning in GPT-3 and -4
Sam Musker, Ellie Pavlick
Large Language Models (LLMs) have driven extraordinary improvements in NLP. However, it is unclear how such models represent lexical concepts-i.e., the meanings of the words they u…
Language Models Implement Simple Word2Vec-style Vector Arithmetic
Jack Merullo, Carsten Eickhoff, Ellie Pavlick
A primary criticism towards language models (LMs) is their inscrutability. This paper presents evidence that, despite their size and complexity, LMs sometimes exploit a simple vect…
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…
Are Language Models Worse than Humans at Following Prompts? It's Complicated
Albert Webson, Alyssa Marie Loo, Qinan Yu +1
Prompts have been the center of progress in advancing language models' zero-shot and few-shot performance. However, recent work finds that models can perform surprisingly well when…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Linearly Mapping from Image to Text Space
Jack Merullo, Louis Castricato, Carsten Eickhoff +1
The extent to which text-only language models (LMs) learn to represent features of the non-linguistic world is an open question. Prior work has shown that pretrained LMs can be tau…
Unit Testing for Concepts in Neural Networks
Charles Lovering, Ellie Pavlick
Many complex problems are naturally understood in terms of symbolic concepts. For example, our concept of "cat" is related to our concepts of "ears" and "whiskers" in a non-arbitra…
Pretraining on Interactions for Learning Grounded Affordance Representations
Jack Merullo, Dylan Ebert, Carsten Eickhoff +1
Lexical semantics and cognitive science point to affordances (i.e. the actions that objects support) as critical for understanding and representing nouns and verbs. However, study…
Do Trajectories Encode Verb Meaning?
Dylan Ebert, Chen Sun, Ellie Pavlick
Distributional models learn representations of words from text, but are criticized for their lack of grounding, or the linking of text to the non-linguistic world. Grounded languag…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
A Novel Corpus of Discourse Structure in Humans and Computers
Babak Hemmatian, Sheridan Feucht, Rachel Avram +7
We present a novel corpus of 445 human- and computer-generated documents, comprising about 27,000 clauses, annotated for semantic clause types and coherence relations that allow fo…
Does Vision-and-Language Pretraining Improve Lexical Grounding?
Tian Yun, Chen Sun, Ellie Pavlick
Linguistic representations derived from text alone have been criticized for their lack of grounding, i.e., connecting words to their meanings in the physical world. Vision-and-Lang…
Frequency Effects on Syntactic Rule Learning in Transformers
Jason Wei, Dan Garrette, Tal Linzen +1
Pre-trained language models perform well on a variety of linguistic tasks that require symbolic reasoning, raising the question of whether such models implicitly represent abstract…
Do Prompt-Based Models Really Understand the Meaning of their Prompts?
Albert Webson, Ellie Pavlick
Recently, a boom of papers has shown extraordinary progress in zero-shot and few-shot learning with various prompt-based models. It is commonly argued that prompts help models to l…