Debiasing Large Vision-Language Models by Ablating Protected Attribute Representations
Published in NeurIPS 2024 SafeGenAI Workshop, 2024
Publications
You can also find my articles on my Google Scholar profile.
Conference Papers
Steering Large Language Models to Evaluate and Amplify Creativity
Published in NeurIPS 2024 Creativity and Generative AI, 2024
We use model steering to measure and enhance creative properties of LLMs.
Exploring Vision Transformers for Early Detection of Climate Change Signals
Published in NeurIPS 2024 Tackling Climate Change with Machine Learning, 2024
AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments
Published in EMNLP 2024 (Main), 2024
We introduce a three-part framework for constructing persuasive messages.
Why do LLaVA Vision-Language Models Reply to Images in English?
Published in EMNLP 2024 (Findings), 2024
Including an image in the query to a LLaVA-style VLM significantly increases the likelihood of the model returning an English response, regardless of the language of the query.
LLaVA-Gemma: Accelerating Multimodal Foundation Models with a Compact Language Model
Published in CVPR 2024 Multimodal Foundation Models Workshop, 2024
We train a suite of multimodal foundation models (MMFM) using the popular LLaVA framework with the recently released Gemma family of large language models (LLMs).
Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models
Published in ACL 2024 (Main), 2024
We evaluate the latent political biases of LLMs, and show that models give substantively different answers when not forced; that answers change depending on how models are forced; and that answers lack paraphrase robustness.
Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Models
Published in NeurIPS 2023 (Main), 2023
We present a new algorithm for using imperfect annotation surrogates for downstream statistical analyses while guaranteeing statistical properties—like asymptotic unbiasedness and proper uncertainty quantification—which are fundamental to Computational Social Science research.