Feb. 9, 2024, 5:47 a.m. | Jiuzhou Han Nigel Collier Wray Buntine Ehsan Shareghi

cs.CL updates on arXiv.org arxiv.org

Large language models (LLMs) have shown great abilities of solving various natural language tasks in different domains. Due to the training objective of LLMs and their pre-training data, LLMs are not very well equipped for tasks involving structured data generation. We propose a framework, Prompting with Iterative Verification (PiVe), to improve graph-based generative capability of LLMs. We show how a small language model could be trained to act as a verifier module for the output of an LLM(i.e., ChatGPT, GPT-4), …

capability cs.cl data domains framework generative graph graph-based iterative language language models large language large language models llms natural natural language pre-training prompting structured data tasks training training data verification

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