Papers by Zhaotian Weng
What’s Missing in Vision-Language Models? Probing Their Struggles with Causal Order Reasoning (2026.eacl-long)
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| Challenge: | Existing benchmarks often include a mixture of reasoning questions, making it difficult to truly assess VLMs’ causal reasoning abilities. |
| Approach: | They propose two new benchmarks specifically designed to isolate and rigorously evaluate VLMs’ causal reasoning abilities. |
| Outcome: | The proposed benchmarks show that vision-language models perform poorly on causal reasoning tasks, often only marginally surpassing random guessing. |
Images Speak Louder than Words: Understanding and Mitigating Bias in Vision-Language Model from a Causal Mediation Perspective (2024.emnlp-main)
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| Challenge: | Current methods to learn biases from the perspective of model components are limited by their complexity and performance. |
| Approach: | They propose a framework that incorporates causal mediation analysis to measure and map the pathways of bias generation and propagation within vision-language and multimodal tasks. |
| Outcome: | The proposed framework is applicable to a wide range of vision-language and multimodal tasks and reduces bias by 22.03% and 9.04% in the MSCOCO and PASCAL-SENTENCE datasets. |