activity
20222026
most citedChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing

135 citations · 183 across the 14 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.CV2025★ 4 cited

Into the Rabbit Hull: From Task-Relevant Concepts in DINO to Minkowski Geometry

Thomas Fel, Binxu Wang, Michael A. Lepori +8

DINOv2 is routinely deployed to recognize objects, scenes, and actions; yet the nature of what it perceives remains unknown. As a working baseline, we adopt the Linear Representati…

cs.CL2025★ 1 cited

Beyond the Rosetta Stone: Unification Forces in Generalization Dynamics

Carter Blum, Katja Filippova, Ann Yuan +8

Large language models (LLMs) struggle with cross-lingual knowledge transfer: they sometimes hallucinate when asked in one language about facts expressed in a different language dur…

cs.AI2025★ 1 cited

Does visualization help AI understand data?

Victoria R. Li, Johnathan Sun, Martin Wattenberg

Charts and graphs help people analyze data, but can they also be useful to AI systems? To investigate this question, we perform a series of experiments with two commercial vision-l…

cs.LG2025

Can Interpretation Predict Behavior on Unseen Data?

Victoria R. Li, Jenny Kaufmann, Tian Qin +3

Interpretability research often predicts model responses to targeted mechanistic interventions. But can we predict responses to unseen input data? We propose and demonstrate this a…

cs.CV2025

Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models

Thomas Fel, Ekdeep Singh Lubana, Jacob S. Prince +7

Sparse Autoencoders (SAEs) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dicti…

cs.LG2025★ 9 cited

Open Problems in Mechanistic Interpretability

Lee Sharkey, Bilal Chughtai, Joshua Batson +26

Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goa…

cs.CL2025★ 1 cited

ICLR: In-Context Learning of Representations

Core Francisco Park, Andrew Lee, Ekdeep Singh Lubana +5

Recent work has demonstrated that semantics specified by pretraining data influence how representations of different concepts are organized in a large language model (LLM). However…