Papers by Jiaying Zhu
Learning to Contrast the Counterfactual Samples for Robust Visual Question Answering (2020.emnlp-main)
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| Challenge: | Existing methods of generating counterfactual samples are not fully utilized in the task of Visual Question Answering (VQA). |
| Approach: | They propose a self-supervised contrastive learning mechanism to learn the relationship between original samples, factual samples and counterfactual samples. |
| Outcome: | The proposed method surpasses state-of-the-art models on the VQA-CP dataset, a diagnostic benchmark for assessing the VQ model’s robustness. |
Quantifying Semantic Emergence in Language Models (2025.acl-long)
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| Challenge: | Existing evaluation methods for large language models (LLMs) focus on coarse-grained text, not providing interpretations for the behavior of finergrained tokens. |
| Approach: | They propose a quantitative metric to measure large language models’ ability to extract semantics from input tokens. |
| Outcome: | The proposed metric compares the entropy reduction observed for a sequence of tokens and individual tokens. |