Audit finds 36-39% incorrect FOL labels in FOLIO and MALLS; corrections raise LLM accuracy 9-22 points and an LLM-guided review framework achieves 90% dataset quality after checking fewer than 24% of examples.
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2 Pith papers cite this work. Polarity classification is still indexing.
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A four-valued intensional first-order logic with a Closed Knowledge Assumption is proposed for AGI robots, treating unlisted facts as 'unknown' and inconsistent statements as the special value ⊤.
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Fixing FOLIO and MALLS: Verified Annotations and an LLM-assisted Framework to Focus Human Relabeling
Audit finds 36-39% incorrect FOL labels in FOLIO and MALLS; corrections raise LLM accuracy 9-22 points and an LLM-guided review framework achieves 90% dataset quality after checking fewer than 24% of examples.
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Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions
A four-valued intensional first-order logic with a Closed Knowledge Assumption is proposed for AGI robots, treating unlisted facts as 'unknown' and inconsistent statements as the special value ⊤.