However, it is nontrivial to directly apply LLMs for EA since the annotation space in real-world KGs is large. LLMs could also generate noisy labels that may mislead the alignment.

Typicalness-A ware Learning (T AL) to address this issue and improve failure detection performance.

In this work, we propose a novel calibration method that can be used to combat hallucinations.

Despite these achievements, LLMs often underperform in domain-specific tasks without training on relevant domain data.

They also prevent people from misusing images, especially those generated by AI models. We propose a family of regeneration attacks to remove these invisible watermarks.