Papers by Davide Mottin
Automatic Annotation Augmentation Boosts Translation between Molecules and Natural Language (2025.findings-naacl)
Copied to clipboard
| Challenge: | Recent advances in AI for biological research focus on integrating molecular data with natural language to accelerate drug discovery. |
| Approach: | They propose a Language-based Automatic Annotation Augmentation framework that leverages large language models to augment existing datasets. |
| Outcome: | The proposed framework outperforms state-of-the-art models on text-based tasks and validates its versatility and utility. |
Harnessing Large Language Models as Post-hoc Correctors (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated their effectiveness in a wide range of tasks, including machine translation and commonsense reasoning. |
| Approach: | They propose a training-free framework that can work as a post-hoc corrector to propose corrections for ML models. |
| Outcome: | The proposed framework improves the performance of a number of models by up to 39% on text analysis and the challenging molecular predictions. |