Papers by Philippe Mulhem
What Matters to an LLM? Behavioral and Computational Evidences from Summarization (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) are increasingly entrusted with the management of information. |
| Approach: | They combine behavioral and computational analyses to find out what LLMs prioritize . they generate length-controlled summaries and derive empirical importance distributions . |
| Outcome: | The proposed model converges on consistent importance patterns and clusters more by family than by size. |
Building Evaluation Datasets for Cultural Microblog Retrieval (L18-1)
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| Challenge: | null |
| Approach: | null |
| Outcome: | null |