Papers by Clement Christophe
Cross-Examination Framework: A Task-Agnostic Diagnostic for Information Fidelity in Text-to-Text Generation (2026.acl-long)
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Tathagata Raha, Clement Christophe, Nada Saadi, Hamza A Javed, Marco AF Pimentel, Ronnie Rajan, Praveenkumar Kanithi
| Challenge: | Traditional metrics like BLEU and BERTScore fail to capture semantic fidelity in generative text-to-text tasks. |
| Approach: | They propose a cross-examination framework that generates verifiable questions from each text and performs a Cross-exam to derive three interpretable scores: Coverage, Conformity, and Consistency. |
| Outcome: | The proposed framework detects critical errors across translation, summarization and clinical note-generation and human expert validation shows it is reliable without gold references. |
Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency (2025.emnlp-main)
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Svetlana Maslenkova, Clement Christophe, Marco AF Pimentel, Tathagata Raha, Muhammad Umar Salman, Ahmed Al Mahrooqi, Avani Gupta, Shadab Khan, Ronnie Rajan, Praveenkumar Kanithi
| Challenge: | Current dataset curation and bias assessment practices lack transparency . current approaches lack a thorough understanding of how data characteristics influence model behavior . |
| Approach: | They propose a comprehensive bias evaluation framework that integrates general benchmarks with a healthcare-specific methodology to probe for biases in a sensitive healthcare context. |
| Outcome: | The proposed approach to bias evaluation leverages established benchmarks and a healthcare-specific methodology. |
Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs. (2024.findings-emnlp)
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Clement Christophe, Tathagata Raha, Svetlana Maslenkova, Muhammad Umar Salman, Praveenkumar Kanithi, Marco Pimentel, Shadab Khan
| Challenge: | Current approaches to adapting large language models to clinical use-cases are limited. |
| Approach: | They investigate the efficacy of four techniques in adapting large language models for clinical use-cases. |
| Outcome: | The proposed techniques show that they improve performance across clinical tasks. |
Coordinated Replay Sample Selection for Continual Federated Learning (2023.emnlp-industry)
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Jack Good, Jimit Majmudar, Christophe Dupuy, Jixuan Wang, Charith Peris, Clement Chung, Richard Zemel, Rahul Gupta
| Challenge: | Continual Federated Learning (CFL) combines decentralized learning with continuous learning . ubiquity of personal devices with a network connection offers rich source of data for learning problems . |
| Approach: | They propose to combine decentralized learning with a continuous learning approach . they propose to coordinate gradient-based replay sample selection across clients . |
| Outcome: | The proposed method shows gains early in the low replay size regime, when the budget for storing past data is small. |