activity
20232025
most citedFIND: A Function Description Benchmark for Evaluating Interpretability Methods

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Eliciting Language Model Behaviors with Investigator Agents

Xiang Lisa Li, Neil Chowdhury, Daniel D. Johnson +4

Language models exhibit complex, diverse behaviors when prompted with free-form text, making it difficult to characterize the space of possible outputs. We study the problem of beh…

cs.CV2024

Nearest Neighbor Normalization Improves Multimodal Retrieval

Neil Chowdhury, Franklin Wang, Sumedh Shenoy +3

Multimodal models leverage large-scale pre-training to achieve strong but still imperfect performance on tasks such as image captioning, visual question answering, and cross-modal…

cs.CV2024

Automatic Discovery of Visual Circuits

Achyuta Rajaram, Neil Chowdhury, Antonio Torralba +2

To date, most discoveries of network subcomponents that implement human-interpretable computations in deep vision models have involved close study of single units and large amounts…

cs.CV2023

Multimodal Neurons in Pretrained Text-Only Transformers

Sarah Schwettmann, Neil Chowdhury, Samuel Klein +2

Language models demonstrate remarkable capacity to generalize representations learned in one modality to downstream tasks in other modalities. Can we trace this ability to individu…

cs.CL2023★ 3 cited

FIND: A Function Description Benchmark for Evaluating Interpretability Methods

Sarah Schwettmann, Tamar Rott Shaham, Joanna Materzynska +5

Labeling neural network submodules with human-legible descriptions is useful for many downstream tasks: such descriptions can surface failures, guide interventions, and perhaps eve…