LogicLLM: Exploring Self-supervised Logic-enhanced Training for Large Language Models

arXiv:2305.13718v3 Announce Type: replace
Abstract: Existing efforts to improve logical reasoning ability of language models have predominantly relied on supervised fine-tuning, hindering generalization to new domains and/or tasks. The development of Large Langauge Models (LLMs) has demonstrated the capacity of compressing abundant knowledge into a single proxy, enabling them to tackle multiple tasks effectively. Our preliminary experiments, nevertheless, show that LLMs do not show capability on logical reasoning. The performance of LLMs on logical reasoning benchmarks is far behind the …

abstract arxiv capacity cs.cl development domains enabling fine-tuning knowledge language language models large langauge models large language large language models llms logic reasoning supervised fine-tuning tasks them training type