3 papers
cs.LG2026
Dense Structural Compression of Transformers via Gauge-Correct Channel Removal
Jed A. Duersch, Naïm Es-Sebbani, Nathanaël Haas +1
Inference energy per token drives the cost and carbon footprint of deployed transformers. It is dominated by dense matrix products that incur fused multiply-accumulate (FMA) operat…
cs.AI2026
Evaluating Robustness of Reasoning Models on Parameterized Logical Problems
Naïm Es-sebbani, Esteban Marquer, Yakoub Salhi +1
Logic provides a controlled testbed for evaluating LLM-based reasoners, yet standard SAT-style benchmarks often conflate surface difficulty (length, wording, clause order) with the…
cs.CL2024
REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learning
Rameez Qureshi, Naïm Es-Sebbani, Luis Galárraga +3
With the introduction of (large) language models, there has been significant concern about the unintended bias such models may inherit from their training data. A number of studies…