Posts by Collection

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.

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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.

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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).

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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.

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ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution

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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.

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Exploring Vision Transformers for Early Detection of Climate Change Signals

Published in NeurIPS 2024 Tackling Climate Change with Machine Learning 2024

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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.

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Debiasing Large Vision-Language Models by Ablating Protected Attribute Representations

Published in NeurIPS 2024 SafeGenAI Workshop 2024

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IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistance

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Semantic Specialization in Moe Appears with Scale: A Study of DeepSeek-R1 Expert Specialization

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