Fernando Pérez-García


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Compositional Zero-Shot Domain Transfer with Text-to-Text Models
Fangyu Liu | Qianchu Liu | Shruthi Bannur | Fernando Pérez-García | Naoto Usuyama | Sheng Zhang | Tristan Naumann | Aditya Nori | Hoifung Poon | Javier Alvarez-Valle | Ozan Oktay | Stephanie L. Hyland
Transactions of the Association for Computational Linguistics, Volume 11

Label scarcity is a bottleneck for improving task performance in specialized domains. We propose a novel compositional transfer learning framework (DoT51) for zero-shot domain transfer. Without access to in-domain labels, DoT5 jointly learns domain knowledge (from masked language modelling of unlabelled in-domain free text) and task knowledge (from task training on more readily available general-domain data) in a multi-task manner. To improve the transferability of task training, we design a strategy named NLGU: We simultaneously train natural language generation (NLG) for in-domain label-to-data generation, which enables data augmentation for self-finetuning and natural language understanding (NLU) for label prediction. We evaluate DoT5 on the biomedical domain and the resource-lean subdomain of radiology, focusing on natural language inference, text summarization, and embedding learning. DoT5 demonstrates the effectiveness of compositional transfer learning through multi-task learning. In particular, DoT5 outperforms the current state-of-the-art in zero-shot transfer by over 7 absolute points in accuracy on RadNLI. We validate DoT5 with ablations and a case study demonstrating its ability to solve challenging NLI examples requiring in-domain expertise.

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Exploring the Boundaries of GPT-4 in Radiology
Qianchu Liu | Stephanie Hyland | Shruthi Bannur | Kenza Bouzid | Daniel Castro | Maria Wetscherek | Robert Tinn | Harshita Sharma | Fernando Pérez-García | Anton Schwaighofer | Pranav Rajpurkar | Sameer Khanna | Hoifung Poon | Naoto Usuyama | Anja Thieme | Aditya Nori | Matthew Lungren | Ozan Oktay | Javier Alvarez-Valle
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

The recent success of general-domain large language models (LLMs) has significantly changed the natural language processing paradigm towards a unified foundation model across domains and applications. In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models. Exploring various prompting strategies, we evaluated GPT-4 on a diverse range of common radiology tasks and we found GPT-4 either outperforms or is on par with current SOTA radiology models. With zero-shot prompting, GPT-4 already obtains substantial gains ( 10% absolute improvement) over radiology models in temporal sentence similarity classification (accuracy) and natural language inference (F1). For tasks that require learning dataset-specific style or schema (e.g. findings summarisation), GPT-4 improves with example-based prompting and matches supervised SOTA. Our extensive error analysis with a board-certified radiologist shows GPT-4 has a sufficient level of radiology knowledge with only occasional errors in complex context that require nuanced domain knowledge. For findings summarisation, GPT-4 outputs are found to be overall comparable with existing manually-written impressions.