Papers by Esther Gan

2 papers
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.
Reasoning Robustness of LLMs to Adversarial Typographical Errors (2024.emnlp-main)

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Challenge: Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning using Chain-of-Thought (CoT) prompting.
Approach: They develop an algorithm that iteratively samples typos for words that are important to the query and selects the edit that is most likely to succeed in attacking.
Outcome: The proposed algorithm detects typographical errors in large and closed-source LLMs and shows that they are robust to them.

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