9 citations · 15 across the 7 of their papers we have counts for
8 papers
Compartmentalised Agentic Reasoning for Clinical NLI
Maël Jullien, Lei Xu, Marco Valentino +1
Large language models can produce fluent judgments for clinical natural language inference, yet they frequently fail when the decision requires the correct inferential schema rathe…
The Knowledge-Reasoning Dissociation: Fundamental Limitations of LLMs in Clinical Natural Language Inference
Maël Jullien, Marco Valentino, André Freitas
Large language models are often assumed to acquire increasingly structured, generalizable internal representations simply by scaling data and parameters. We interrogate this assump…
Dissecting Clinical Reasoning in Language Models: A Comparative Study of Prompts and Model Adaptation Strategies
Mael Jullien, Marco Valentino, Leonardo Ranaldi +1
Recent works on large language models (LLMs) have demonstrated the impact of prompting strategies and fine-tuning techniques on their reasoning capabilities. Yet, their effectivene…
Controlled LLM-based Reasoning for Clinical Trial Retrieval
Mael Jullien, Alex Bogatu, Harriet Unsworth +1
Matching patients to clinical trials demands a systematic and reasoned interpretation of documents which require significant expert-level background knowledge, over a complex set o…
SemEval-2024 Task 2: Safe Biomedical Natural Language Inference for Clinical Trials
Mael Jullien, Marco Valentino, André Freitas
Large Language Models (LLMs) are at the forefront of NLP achievements but fall short in dealing with shortcut learning, factual inconsistency, and vulnerability to adversarial inpu…
SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data
Maël Jullien, Marco Valentino, Hannah Frost +3
This paper describes the results of SemEval 2023 task 7 -- Multi-Evidence Natural Language Inference for Clinical Trial Data (NLI4CT) -- consisting of 2 tasks, a Natural Language I…
NLI4CT: Multi-Evidence Natural Language Inference for Clinical Trial Reports
Maël Jullien, Marco Valentino, Hannah Frost +3
How can we interpret and retrieve medical evidence to support clinical decisions? Clinical trial reports (CTR) amassed over the years contain indispensable information for the deve…
Do Transformers Encode a Foundational Ontology? Probing Abstract Classes in Natural Language
Mael Jullien, Marco Valentino, Andre Freitas
With the methodological support of probing (or diagnostic classification), recent studies have demonstrated that Transformers encode syntactic and semantic information to some exte…