1 citations · 1 across the 3 of their papers we have counts for
5 papers
Which Attention Heads are like the Human Head? Not the Ones that Compute
Christopher Pinier, Gustaw Opiełka, Hannes Rosenbusch +3
Brain-AI alignment is often interpreted as a sign that model and brain perform similar computations. Whether the aligned units are causally involved in model computation is rarely…
Causality Invariance: Function and Concept Vectors in LLMs
Gustaw Opiełka, Hannes Rosenbusch, Claire E. Stevenson
Do large language models (LLMs) represent concepts abstractly, i.e., independent of input format? We revisit Function Vectors (FVs), compact representations of in-context learning…
Analogical Reasoning Inside Large Language Models: Concept Vectors and the Limits of Abstraction
Gustaw Opiełka, Hannes Rosenbusch, Claire E. Stevenson
Analogical reasoning relies on conceptual abstractions, but it is unclear whether Large Language Models (LLMs) harbor such internal representations. We explore distilled representa…
Saliency Suppressed, Semantics Surfaced: Visual Transformations in Neural Networks and the Brain
Gustaw Opiełka, Jessica Loke, Steven Scholte
Deep learning algorithms lack human-interpretable accounts of how they transform raw visual input into a robust semantic understanding, which impedes comparisons between different…
Do Large Language Models Solve ARC Visual Analogies Like People Do?
Gustaw Opiełka, Hannes Rosenbusch, Veerle Vijverberg +1
The Abstraction Reasoning Corpus (ARC) is a visual analogical reasoning test designed for humans and machines (Chollet, 2019). We compared human and large language model (LLM) perf…