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cs.CL2026
SSA: Improving Performance With a Better Scoring Function
Omar Naim, Swarnadeep Bhar, Jérôme Bolte +1
While transformer models exhibit strong in-context learning (ICL) abilities, they often fail to generalize under simple distribution shifts. We analyze these failures and identify…
cs.CL2026
COCORELI: Enforcing Execution Preconditions for Reliable Collaborative Instruction Following
Swarnadeep Bhar, Omar Naim, Eleni Metheniti +4
Autonomous agents executing human instructions must operate reliably even when instructions are incomplete. While recent approaches improve detection of missing information, detect…
cs.CL2024
On Explaining with Attention Matrices
Omar Naim, Nicholas Asher
This paper explores the much discussed, possible explanatory link between attention weights (AW) in transformer models and predicted output. Contrary to intuition and early researc…