Papers with PGR
A Silver Standard Corpus of Human Phenotype-Gene Relations (N19-1)
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| Challenge: | Existing tools for phenotype-gene relations extraction require annotated corpus, which requires manual effort and time. |
| Approach: | They propose to generate a silver standard corpus of human phenotype and gene annotations and their relations using Named-Entity Recognition tools. |
| Outcome: | The proposed corpus was generated with Named-Entity Recognition tools with a precision of 87.01%. |
Fact Verification on Knowledge Graph via Programmatic Graph Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for fact verification on knowledge graphs use implicit reasoning to predict entailment between claims and KG triples. |
| Approach: | They propose a framework that integrates large language models for fact verification on knowledge graphs. |
| Outcome: | The proposed framework outperforms existing methods on knowledge graphs with 86.82% accuracy. |
How to Mitigate Overfitting in Weak-to-strong Generalization? (2025.acl-long)
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| Challenge: | Experimental results show that weak-to-strong generalization significantly improves PGR compared to naive weak- to-strong . superalignment refers to how humans can align models on tasks beyond human ability to evaluate . |
| Approach: | They propose a framework that elicits the capabilities of strong models through weak supervisors . they propose 'superalignment' to ensure that strong models align with supervisors' intentions . |
| Outcome: | The proposed framework significantly improves quality of supervision signals and quality of input questions compared to naive weak-to-strong generalization . |