PhantomBench is a new benchmark of 60K+ non-existent terms showing language models hallucinate at rates up to 86.7 percent even when inputs assume the concepts exist.
Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models
4 Pith papers cite this work, alongside 13 external citations. Polarity classification is still indexing.
4
Pith papers citing it
13
external citations · OpenAlex
years
2026 4representative citing papers
Frontier LLMs struggle to discriminate data uncertainty from model uncertainty even when accurate, but a new benchmark and lightweight RL strategy improve attribution without sacrificing answer accuracy.
Counterfactual no-search vs forced-search outcomes yield a model-specific oracle that trains search-routing policies, raising macro-F1 from ~0.71 to ~0.82–0.84 on oracle-eligible examples.
JTS trains reasoning models via supervised warm-up and missing-premise RL to make an explicit answerability commitment that triggers early termination on unanswerable inputs, raising Abstention@Detection near saturation.
citing papers explorer
-
PhantomBench: Benchmarking the Non-existential Threat of Language Models
PhantomBench is a new benchmark of 60K+ non-existent terms showing language models hallucinate at rates up to 86.7 percent even when inputs assume the concepts exist.
-
Beyond "I Don't Know": Evaluating LLM Self-Awareness in Discriminating Data and Model Uncertainty
Frontier LLMs struggle to discriminate data uncertainty from model uncertainty even when accurate, but a new benchmark and lightweight RL strategy improve attribution without sacrificing answer accuracy.
-
When Should LLMs Search? Counterfactual Supervision for Search Routing
Counterfactual no-search vs forced-search outcomes yield a model-specific oracle that trains search-routing policies, raising macro-F1 from ~0.71 to ~0.82–0.84 on oracle-eligible examples.
-
Bridging the Detection-to-Abstention Gap in Reasoning Models under Insufficient Information
JTS trains reasoning models via supervised warm-up and missing-premise RL to make an explicit answerability commitment that triggers early termination on unanswerable inputs, raising Abstention@Detection near saturation.