6 papers
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…
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…
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…
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…
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…