Prof. Howard and his team (Dr. Kim, Jung Hyun, and David) presented their work, "CReSE: Benchmark Data and Automatic Evaluation Framework for Recommending Eligibility Criteria from Clinical Trial Information," at the 2024 European Chapter of the Association for Computational Linguistics (EACL). The presentation was well received and sparked lively debates among the attendees.

The EACL conference, hosted in Malta from March 17th to 22nd, was not just a venue for presenting their findings; it also provided enriching learning opportunities. The event covered a wide range of topics, from the challenges posed by large language models (LLMs), as seen in studies like "Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs," to their innovative applications in healthcare, as exemplified by "Leveraging ChatGPT in Pharmacovigilance Event Extraction: An Empirical Study." These discussions spurred our team to consider how we could integrate these insights into our research.

Additionally, the conference featured thought-provoking keynote speeches that challenged prevailing views on language models. Dr. Lapata, in particular, argued against the notion that LLMs are a solution-for-all. Instead, she advocated for a modular approach, employing specialized, smaller language models to efficiently and sustainably address various natural language processing tasks, such as summarizing and crosslingual semantic parsing.

The main conference, spanning three days, was followed by workshops that offered practical learning experiences. "Computational Approaches to Language Data Pseudonymization" was notably relevant to our work, focusing on the anonymization of sensitive text data, a critical consideration when dealing with clinical texts. A standout session, "When Is a Name Sensitive? Eponyms in Clinical Text and Implications for De-Identification," delved into how transformer-based models could minimize false positives in de-identifying clinical texts, a concern often raised by the presence of eponyms, and it got us thinking deeply about how to properly deidentify personal health information in clinical notes.

All in all, the week-long EACL conference served dual purposes: it was an avenue for sharing our contributions with the NLP community and a fertile ground for acquiring new knowledge. Such opportunities are invaluable, affirming our progress and direction in the field.