Papers by Marco Lepri
Do LLMs suffer from Multi-Party Hangover? A Diagnostic Approach to Addressee Recognition and Response Selection in Conversations (2024.emnlp-main)
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| Challenge: | Conventional evaluation methods often overlook variances in model behavior across different levels of structural complexity on interaction graphs. |
| Approach: | They propose a methodological pipeline to investigate model performance across structural attributes of conversations. |
| Outcome: | The proposed method analyzes the performance of an LLM to classify multi-party conversations . it shows that response selection relies more on the textual content of conversations compared to addressee recognition . |
Putting Context in Context: the Impact of Discussion Structure on Text Classification (2024.eacl-long)
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| Challenge: | Current text classification approaches focus on the content to be classified, but contextual information is neglected in many cases. |
| Approach: | They propose to integrate contextual information into a transformer-based model by feeding it as natural language input into . they also experiment with different amounts of training data and analyse local discussion networks in a privacy-compliant way. |
| Outcome: | The proposed model can be generalized to other datasets and is privacy-compliant. |
MEDDxAgent: A Unified Modular Agent Framework for Explainable Automatic Differential Diagnosis (2025.acl-long)
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Daniel Philip Rose, Chia-Chien Hung, Marco Lepri, Israa Alqassem, Kiril Gashteovski, Carolin Lawrence
| Challenge: | Existing approaches to DDx are limited by single-dataset evaluations, isolated optimization of components, unrealistic assumptions about complete patient profiles, and single-attempt diagnosis. |
| Approach: | They propose a Modular Explainable DDx Agent framework that allows physicians to iteratively refine a ranked list of possible diseases based on symptoms, antecedents, and medical knowledge. |
| Outcome: | The proposed framework achieves over 10% accuracy improvements in interactive DDx across large and small LLMs while offering critical explainability into its diagnostic reasoning process. |