CiteCheck detects LLM citation hallucinations via retrieval and LLM-based comparison, achieving 88.7 macro-F1 on a 982-citation benchmark.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
DeepSciVerify achieves 86.7 Micro-F1 on SCitance for claim-citation verification via abstract-level reasoning plus selective full-text escalation, beating abstract-only baselines by 4.5 points and skipping full-text retrieval in 67% of cases.
The authors propose a multitask GLiNER framework with synthetic data and LLM revalidation for scalable monitoring and classification of dataset usage in academic literature.
citing papers explorer
-
CiteCheck: Retrieval-Grounded Detection of LLM Citation Hallucinations in Scientific Text
CiteCheck detects LLM citation hallucinations via retrieval and LLM-based comparison, achieving 88.7 macro-F1 on a 982-citation benchmark.
-
DeepSciVerify: Verifying Scientific Claim--Citation Alignment via LLM-Driven Evidence Escalation
DeepSciVerify achieves 86.7 Micro-F1 on SCitance for claim-citation verification via abstract-level reasoning plus selective full-text escalation, beating abstract-only baselines by 4.5 points and skipping full-text retrieval in 67% of cases.
-
AI for Monitoring and Classifying Data Used in Research Literature
The authors propose a multitask GLiNER framework with synthetic data and LLM revalidation for scalable monitoring and classification of dataset usage in academic literature.