Papers with validation metrics
Eye Movement Features Can Predict Human Preferences on Machine-Generated Texts (2026.acl-srw)
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| Challenge: | Existing studies on eye movement in text quality assessment are limited . eye-movement features are important predictors of human judgments of text quality, but are costly and inconsistent. |
| Approach: | They propose to capture eye-movement features during screen reading of LLM-generated text using a dataset that includes eye-motion recordings, reading-time measurements, and post-reading evaluations. |
| Outcome: | The proposed dataset shows that eye-movement features can significantly improve models over other probabilistic metrics, including negative log-likelihood (NLL). |
Medical Knowledge-enriched Textual Entailment Framework (2020.coling-main)
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| Challenge: | Existing approaches to achieving robust medical question answering systems lack a textual entailment framework that can capture the con-text beyond the sentence. |
| Approach: | They propose a medical knowledge-enriched textual entailment framework that can acquire a semantic and global representation of the input medical text with the help of a relevant domain-specific knowledge graph. |
| Outcome: | The proposed framework achieves 8.27% improvement over existing language models on MEDIQA-RQE dataset. |