KARL uses a knowledge-boundary-aware reward from within-group response statistics and two-stage RL training to align LLM abstention with actual knowledge, yielding a better accuracy-hallucination trade-off on benchmarks.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
KARL: Mitigating Hallucinations in LLMs via Knowledge-Boundary-Aware Reinforcement Learning
KARL uses a knowledge-boundary-aware reward from within-group response statistics and two-stage RL training to align LLM abstention with actual knowledge, yielding a better accuracy-hallucination trade-off on benchmarks.