9 papers
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Gabrielle Kaili-May Liu, Avi Caciularu, Gal Yona +2
Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes. Yet LLMs exhibit systemic deficiencies in ke…
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…
Stop When Reasoning Converges: Semantic-Preserving Early Exit for Reasoning Models
Dehai Min, Giovanni Vaccarino, Huiyi Chen +3
Large Reasoning Models (LRMs) achieve strong performance by generating long chains of thought (CoT), but often overthink, continuing to reason after a solution has already stabiliz…
Hallucinations Undermine Trust; Metacognition is a Way Forward
Gal Yona, Mor Geva, Yossi Matias
Despite significant strides in factual reliability, errors -- often termed hallucinations -- remain a major concern for generative AI, especially as LLMs are increasingly expected…
SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge
Lukas Haas, Gal Yona, Giovanni D'Antonio +2
We introduce SimpleQA Verified, a 1,000-prompt benchmark for evaluating Large Language Model (LLM) short-form factuality based on OpenAI's SimpleQA. It addresses critical limitatio…
MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs
Gabrielle Kaili-May Liu, Gal Yona, Avi Caciularu +3
A critical component in the trustworthiness of LLMs is reliable uncertainty communication, yet LLMs often use assertive language when conveying false claims, leading to over-relian…