{"labels":{"en":"Rule enforcement in LLMs: a parameter efficient fine-tuning approach with self-generated training dataset"},"descriptions":{"en":"scientific paper published in CEUR-WS Volume 3903"},"claims":{"P31":"Q13442814","P1433":"Q134029000","P1476":{"text":"Rule enforcement in LLMs: a parameter efficient fine-tuning approach with self-generated training dataset","language":"en"},"P407":"Q1860","P953":"https://ceur-ws.org/Vol-3903/AIxHMI2024_paper3.pdf","P50":[],"P2093":[{"value":"Daniele Franch","qualifiers":{"P1545":"1"}},{"value":"Pierluigi Roberti","qualifiers":{"P1545":"2"}},{"value":"Enrico Blanzieri","qualifiers":{"P1545":"3"}}]}}