Papers by Emerson Liu
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training (2025.findings-acl)
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Yihang Yao, Zhepeng Cen, Miao Li, William Han, Yuyou Zhang, Emerson Liu, Zuxin Liu, Chuang Gan, Ding Zhao
| Challenge: | Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks. |
| Approach: | They propose a data-centric approach that enhances LLMs’ awareness of symmetry in query variations and propose syMmetry-ENhanceD (MEND) data augmentation. |
| Outcome: | Extensive experiments on logical and arithmetic reasoning tasks show that the proposed approach improves model robustness at the knowledge extraction stage through query augmentation. |
Learning Functional Distributional Semantics with Visual Data (2022.acl-long)
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| Challenge: | Functional Distributional Semantics models the meaning of a word as a binary classifier rather than a numerical vector. |
| Approach: | They propose a method to train a Functional Distributional Semantics model with grounded visual data. |
| Outcome: | The proposed model outperforms previous work on learning semantics from Visual Genome on four external evaluation datasets. |
Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models? (2023.findings-eacl)
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Jielin Qiu, William Han, Jiacheng Zhu, Mengdi Xu, Michael Rosenberg, Emerson Liu, Douglas Weber, Ding Zhao
| Challenge: | Recent advances in Large Language Models (LLMs) have shown powerful ability in various downstream applications. |
| Approach: | They propose an approach for cardiovascular disease diagnosis and automatic ECG diagnosis report generation. |
| Outcome: | The proposed approach generates high-quality cardiac diagnosis reports and achieves competitive zero-shot classification performance even compared with supervised baselines. |
Visual Spatial Reasoning (2023.tacl-1)
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| Challenge: | Existing benchmarks for testing vision-language models (VLMs) are not ideal as they conflate multiple sources of error and do not allow controlled analysis on specific linguistic or cognitive properties. |
| Approach: | They present a dataset containing more than 10k natural text-image pairs with 66 types of spatial relations in English (e.g., under, in front of, facing). |
| Outcome: | The proposed model fails to capture relational information in a visual question answering task and referring expression comprehension tasks. |