Papers by Yunqing Li
OG-RAG: Ontology-grounded retrieval-augmented generation for large language models (2025.emnlp-main)
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| Challenge: | Existing retrieval-augmented models fail to account for structured domain knowledge, leading to suboptimal context generation. |
| Approach: | They propose an Ontology-Grounded Retrieval Augmented Generation method to enhance LLM-generated responses by anchoring retrieval processes in domain-specific ontologies. |
| Outcome: | The proposed method increases recall of accurate facts by 55% and improves response correctness by 40% across four different LLMs. |
GLProtein: Global-and-Local Structure Aware Protein Representation Learning (2025.findings-emnlp)
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| Challenge: | Despite advances in protein sequence analysis, there remains potential for further exploration in integrating protein structural information. |
| Approach: | They propose a framework that integrates global structural similarity and local amino acid details to enhance protein pre-training. |
| Outcome: | The proposed framework outperforms existing methods in several bioinformatics tasks. |