Papers by Robert Mercer
Graph-tree Fusion Model with Bidirectional Information Propagation for Long Document Classification (2024.findings-emnlp)
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| Challenge: | Existing methods for document classification struggle with token limits and fail to adequately model hierarchical relationships within documents. |
| Approach: | They propose a novel model leveraging a graph-tree structure to capture local and global dependencies. |
| Outcome: | The proposed model captures syntactic relationships and broader document contexts without token limits and can handle arbitrarily long contexts. |
Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation (2024.naacl-long)
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| Challenge: | Existing methods for ICD indexing have a heavy label distribution and a manual process . Xie and Xing (2017) propose a new approach to ICD re-ranking . |
| Approach: | They propose a "retrieve and re-rank" framework to allocate subsets of ICD codes to medical records . they leverage auxiliary knowledge of the electronic health records (EHR) and a discrete retrieval method . |
| Outcome: | The proposed method achieves state-of-the-art performance on the MIMIC-III benchmark. |
Anisotropy is Not Inherent to Transformers (2024.naacl-long)
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| Challenge: | Existing work has shown that Transformer embeddings are anisotropic, which is called the representation degradation problem. |
| Approach: | They identify a set of Transformer models with isotropic embedding spaces, the large Pythia models. |
| Outcome: | The proposed model sets show that isotropic models do not develop as previously theorized. |
KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling (2022.acl-long)
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| Challenge: | Medical Subject Headings (MeSH) are manually assigned to every biomedical article to facilitate retrieval of relevant information. |
| Approach: | They propose a model that combines new text features with a dynamic knowledge-enhanced mask attention that integrates document features with MeSH label hierarchy and journal correlation features to index MeSH terms. |
| Outcome: | The proposed model achieves state-of-the-art on a number of measures. |
Trustworthy Medical Question Answering: An Evaluation-Centric Survey (2025.emnlp-main)
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Yinuo Wang, Baiyang Wang, Robert Mercer, Frank Rudzicz, Sudipta Singha Roy, Pengjie Ren, Zhumin Chen, Xindi Wang
| Challenge: | achieving comprehensive trustworthiness in medical QA poses significant challenges due to complexity of healthcare data, critical nature of clinical scenarios, and multifaceted dimensions of trustworthy AI. |
| Approach: | They examine six key dimensions of trustworthiness in medical QA . they compare how each dimension is evaluated in existing LLM-based systems . |
| Outcome: | The findings show that large language models have improved patient safety and effectiveness . the models exhibit critical trust failures when deployed in clinical settings . |