8 papers
Recon: Reconstruction-Guided Reasoning Synthesis for User Modeling
Alan Zhu, Mihran Miroyan, Carolyn Wang +4
User modeling aims to use language models (LMs) to mimic an individual's behavior from a corpus of past context-action pairs (e.g., conversation turns), enabling the simulation of…
VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents
Zirui Wang, Junyi Zhang, Jiaxin Ge +9
Modern Vision-Language Models (VLMs) remain poorly characterized in multi-step visual interactions, particularly in how they integrate perception, memory, and action over long hori…
Visually Prompted Benchmarks Are Surprisingly Fragile
Haiwen Feng, Long Lian, Lisa Dunlap +6
A key challenge in evaluating VLMs is testing models' ability to analyze visual content independently from their textual priors. Recent benchmarks such as BLINK probe visual percep…
Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit
Nick Jiang, Xiaoqing Sun, Lisa Dunlap +2
Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors or biases in training data. Current metho…
Discovering Divergent Representations between Text-to-Image Models
Lisa Dunlap, Joseph E. Gonzalez, Trevor Darrell +3
In this paper, we investigate when and how visual representations learned by two different generative models diverge. Given two text-to-image models, our goal is to discover visual…
VibeCheck: Discover and Quantify Qualitative Differences in Large Language Models
Lisa Dunlap, Krishna Mandal, Trevor Darrell +2
Large language models (LLMs) often exhibit subtle yet distinctive characteristics in their outputs that users intuitively recognize, but struggle to quantify. These "vibes" -- such…