HalluHunter is a knowledge-graph and rule-based NLP framework that iteratively generates single- and multi-hop questions to uncover factual errors in LLMs, triggering errors in up to 55% of cases on nine models while preserving coverage.
Brown, Miljan Martic, Shane Legg, and Dario Amodei
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.SE 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Identifying the Achilles' Heel: An Iterative Method for Dynamically Uncovering Factual Errors in Large Language Models
HalluHunter is a knowledge-graph and rule-based NLP framework that iteratively generates single- and multi-hop questions to uncover factual errors in LLMs, triggering errors in up to 55% of cases on nine models while preserving coverage.