7 citations · 22 across the 20 of their papers we have counts for
5 papers · 1 filter
Encoded but Unused: Bounding the Sources of VLM Errors
Anat Veikherman, Mor Ventura, Nitay Calderon +2
In this paper, we investigate why vision-language models (VLMs) produce errors by decomposing them into two sources: visual-representation failures, in which the visual evidence re…
LLM Explainability with Counterfactual Chains and Causal Graphs
Nirit Nussbaum-Hoffer, Nitay Calderon, Liat Ein-Dor +1
Causal graphs provide a high-level language for making mechanisms transparent. Recent work uses Large Language Models (LLMs) to recover causal graphs of external-world processes. I…
A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks
Tomer Keren, Nitay Calderon, Asaf Yehudai +3
As agent capabilities advance, existing benchmarks, such as -Bench, are becoming increasingly saturated. Yet constructing new benchmark tasks remains complex, costly, and labo…
Empty Shelves or Lost Keys? Recall Is the Bottleneck for Parametric Factuality
Nitay Calderon, Eyal Ben-David, Zorik Gekhman +2
Standard factuality evaluations of LLMs treat all errors alike, obscuring whether failures arise from missing knowledge (empty shelves) or from limited access to encoded facts (los…
LIBERTy: A Causal Framework for Benchmarking Concept-Based Explanations of LLMs with Structural Counterfactuals
Gilat Toker, Nitay Calderon, Ohad Amosy +1
Concept-based explanations quantify how high-level concepts (e.g., gender or experience) influence model behavior, which is crucial for decision-makers in high-stakes domains. Rece…