21 citations · 96 across the 26 of their papers we have counts for
27 papers
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…
Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness
Tomer Ashuach, Shai Gretz, Yoav Katz +2
Humans use introspection to evaluate their understanding through private internal states inaccessible to external observers. We investigate whether large language models possess si…
General Agent Evaluation
Elron Bandel, Asaf Yehudai, Lilach Eden +12
General-purpose agents perform tasks in unfamiliar environments without domain-specific manual customization. Yet no study has systematically measured how agent architecture shapes…
Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs
Yehonatan Peisakhovsky, Zorik Gekhman, Yosi Mass +2
Context-grounded hallucinations are cases where model outputs contain information not verifiable against the source text. We study the applicability of LLMs for localizing such hal…
Multi-Domain Explainability of Preferences
Nitay Calderon, Liat Ein-Dor, Roi Reichart
Preference mechanisms, such as human preference, LLM-as-a-Judge (LaaJ), and reward models, are central to aligning and evaluating large language models (LLMs). Yet, the underlying…
WildIFEval: Instruction Following in the Wild
Gili Lior, Asaf Yehudai, Ariel Gera +1
Recent LLMs have shown remarkable success in following user instructions, yet handling instructions with multiple constraints remains a significant challenge. In this work, we intr…