Papers by Lorenzo Jaime Yu Flores
Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding (2023.findings-emnlp)
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| Challenge: | Text simplification has emerged as an increasingly useful application of AI for bridging the communication gap in specialized fields such as medicine, where the lexicon is often dominated by technical jargon and complex constructs. |
| Approach: | They propose a unlikelihood loss that encourages generation of simpler terms and a reranked beam search decoding method that optimizes for simplicity. |
| Outcome: | The proposed methods achieve better performance on readability metrics on three datasets. |
Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution’s Characteristics (2025.acl-short)
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| Challenge: | Existing methods for estimating confidence in text generation do not account for many valid answers in generation tasks. |
| Approach: | They propose task-agnostic confidence metrics that rely solely on model probabilities without the need for further fine-tuning or heuristics. |
| Outcome: | The proposed models improve the accuracy of BART and Flan-T5 on summarization, translation, and question answering datasets. |