Papers by Eric Chamoun
Automated Fact-Checking in Dialogue: Are Specialized Models Needed? (2023.emnlp-main)
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| Challenge: | Prior work has shown that typical fact-checking models struggle with claims made in conversation. |
| Approach: | They propose to fine-tune models for dialogue on conversational data to improve performance on typical fact-checking. |
| Outcome: | The proposed models perform better on stand-alone claims than state-of-the-art models for dialogue while maintaining their performance on standalone claim. |
Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts (2025.emnlp-main)
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| Challenge: | Recent studies show that few papers explicitly identify key stakeholders, intended uses, or appropriate contexts. |
| Approach: | They propose to automate analysis of NLP research by extracting key elements and linking them through interpretable rules and contextual reasoning. |
| Outcome: | The proposed system improves on two domains of fact-checking and hate speech detection. |
Automated Focused Feedback Generation for Scientific Writing Assistance (2024.findings-acl)
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| Challenge: | Recent work has focused on improving surface form and style rather than manuscript content. |
| Approach: | They propose to use a scientific writing focused feedback tool to generate specific, actionable and coherent comments which identify weaknesses in a paper and/or propose revisions to it. |
| Outcome: | The proposed tool outperforms existing approaches in specificity, reading comprehension and overall helpfulness of the generated reviews. |
MPTA: MultiTask Personalization Assessment (2025.findings-emnlp)
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| Challenge: | MTPA tests large language models on real personas spanning demographics, beliefs, and values . aggregate metrics suggest models are truthful and safe, subgroup-specific evaluations reveal hidden pockets of degraded factuality, fairness disparities, and inconsistent value alignment. |
| Approach: | a benchmark is a tool that leverages large-scale survey data to construct real personas . they show persona conditioning exposes pluralistic misalignment . |
| Outcome: | MTPA conditions models on real personas and tests their behavior across alignment tasks. |