Papers by Michela Lorandi
High-quality Data-to-Text Generation for Severely Under-Resourced Languages with Out-of-the-box Large Language Models (2024.findings-eacl)
Copied to clipboard
| Challenge: | Pretrained large language models (LLMs) can bridge the performance gap for under-resourced languages by substantial margins, as measured by both automatic and human evaluations. |
| Approach: | They propose to use pretrained large language models to bridge this gap by automating and evaluating data-to-text generation in under-resourced languages. |
| Outcome: | The proposed model can set the state of the art for under-resourced languages by substantial margins, as measured by both automatic and human evaluations. |
Automatic Paper Analysis and Categorisation for Systematic Reviews with Combined Reasoning-Augmented SFT and DAPO RL (2026.findings-acl)
Copied to clipboard
| Challenge: | Automating systematic reviews is expensive and time consuming, a study finds . automatic approaches are being explored but their performance has been poor . |
| Approach: | They propose to use reasoning-enhanced fine-tuning and DAPO reinforcement learning to automate systematic reviews. |
| Outcome: | The proposed methods significantly improve the performance of LLMs, the authors find . they find that reasoning-enhanced fine-tuning reduces time required for annotation by 80% . |
Enhancing Study-Level Inference from Clinical Trial Papers via Reinforcement Learning-Based Numeric Reasoning (2025.emnlp-main)
Copied to clipboard
| Challenge: | Prior work has framed this task as a textual inference task by retrieving relevant content fragments and inferring conclusions from them. |
| Approach: | They propose to extract structured numerical evidence and apply domain knowledge informed logic to derive outcome-specific conclusions. |
| Outcome: | The proposed approach outperforms general-purpose LLMs of over 400B parameters and achieves a 21% improvement in F1 score over retrieval-based systems. |