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Co-evolving knowledge bases with reasoning capabilities can dramatically enhance the accuracy and relevance of answers in knowledge-intensive tasks.
CO-LMLM achieves lower perplexity than models trained on 40 times more data, revolutionizing how we leverage knowledge bases in language generation.
Forget expensive human annotations or hallucination-prone LLM-generated data: rule-generated synthetic data can teach LLMs to compose knowledge and significantly boost multi-hop reasoning on real-world tasks.
