Papers by Hannah Brown
ComicVQA: A Benchmark for Visual Reasoning in Multimodal LLMs (2026.findings-acl)
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| Challenge: | ComicVQA is a visual reasoning benchmark for comics. |
| Approach: | They propose a comics-based benchmark for evaluating MLLMs on visual reasoning. |
| Outcome: | The proposed model achieves 62.6% accuracy on Missing Panel Prediction and 46.4% on Panel Sorting, compared to open-source models. |
Prompt Optimization via Adversarial In-Context Learning (2024.acl-long)
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Do Long, Yiran Zhao, Hannah Brown, Yuxi Xie, James Zhao, Nancy Chen, Kenji Kawaguchi, Michael Shieh, Junxian He
| Challenge: | Existing methods to optimize prompts for in-context learning are based on adversarial learning and are computationally efficient and extensible to other LLMs and tasks. |
| Approach: | They propose a method to optimize prompts for in-context learning by a generator and a discriminator. |
| Outcome: | The proposed method improves state-of-the-art prompt optimization techniques on 13 generation and classification tasks including summarization, arithmetic reasoning, machine translation, data-to-text generation, and the MMLU and big-bench hard benchmarks. |