Papers by Huamin Qu
Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering (2025.emnlp-main)
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| Challenge: | Misleading visualizations can distort perception and lead to incorrect conclusions. |
| Approach: | They propose a large-scale multimodal dataset to evaluate MLLMs on misleading chart reasoning. |
| Outcome: | The proposed framework evaluates MLLMs on misleading chart reasoning on a large-scale multimodal dataset spanning 21 misleader types and 10 chart types . it contains 3,026 curated examples spanning standard chart code, CSV data, multiple-choice questions, and labeled explanations, validated through iterative MLML checks and exhausted expert human review. |
Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations (2026.findings-acl)
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| Challenge: | Existing benchmarks measure ToM capability improvement through story-reading, multiple-choice questions from a third-person perspective, while ignoring the first-person, dynamic nature of human-AI interactions. |
| Approach: | They propose a new paradigm of interactive ToM evaluation with both perspective and metric shifts. |
| Outcome: | The proposed approach improves the performance of four representative LLM enhancement techniques using real-world datasets and a user study. |
CLLMate: A Multimodal Benchmark for Weather and Climate Events Forecasting (2025.emnlp-main)
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| Challenge: | Existing environmental forecasting research focuses on predicting numerical meteorological variables, neglecting the translation of these variables into actionable textual narratives of events and their consequences. |
| Approach: | They propose a task that leverages numerical meteorological raster data and textual event data to predict weather and climate events. |
| Outcome: | The proposed task leverages numerical meteorological raster data and textual event data to predict weather and climate events. |