35 citations · 48 across the 8 of their papers we have counts for
8 papers
Representational Similarity and Model Behavior in Multi-Agent Interaction
Yujin Potter, Seun Eisape, Shiyang Lai +6
Researchers have shown that neural similarity among humans predicts social closeness and cooperative success, whereas innovation often emerges from interactions among dissimilar in…
Neologism Learning for Controllability and Self-Verbalization
John Hewitt, Oyvind Tafjord, Robert Geirhos +1
Humans invent new words when there is a rising demand for a new useful concept (e.g., doomscrolling). We explore and validate a similar idea in our communication with LLMs: introdu…
How many classes do we need to see for novel class discovery?
Akanksha Sarkar, Been Kim, Jennifer J. Sun
Novel class discovery is essential for ML models to adapt to evolving real-world data, with applications ranging from scientific discovery to robotics. However, these datasets cont…
Escaping Plato's Cave: JAM for Aligning Independently Trained Vision and Language Models
Lauren Hyoseo Yoon, Yisong Yue, Been Kim
Independently trained vision and language models inhabit disjoint representational spaces, shaped by their respective modalities, objectives, and architectures. The Platonic Repres…
Because we have LLMs, we Can and Should Pursue Agentic Interpretability
Been Kim, John Hewitt, Neel Nanda +2
The era of Large Language Models (LLMs) presents a new opportunity for interpretability--agentic interpretability: a multi-turn conversation with an LLM wherein the LLM proactively…
We Can't Understand AI Using our Existing Vocabulary
John Hewitt, Robert Geirhos, Been Kim
This position paper argues that, in order to understand AI, we cannot rely on our existing vocabulary of human words. Instead, we should strive to develop neologisms: new words tha…
Getting aligned on representational alignment
Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller +30
Biological and artificial information processing systems form representations of the world that they can use to categorize, reason, plan, navigate, and make decisions. How can we m…
State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding
Devleena Das, Sonia Chernova, Been Kim
As more non-AI experts use complex AI systems for daily tasks, there has been an increasing effort to develop methods that produce explanations of AI decision making that are under…