Abstract
We present a novel neurosymbolic framework for RDF-to-text generation, in which the model is "trained" through collaborative interactions among multiple LLM agents rather than traditional backpropagation. The LLM agents produce rule-based Python code for a generator for the given domain, based on RDF triples only, with no in-domain human reference texts. The resulting system is fully interpretable, requires no supervised training data, and generates text nearly instantaneously using only a single CPU. Our experiments on the WebNLG and OpenDialKG data show that outputs produced by our approach reduce hallucination, with only slight fluency penalties compared to finetuned or prompted language models
Abstract (translated)
我们提出了一种新颖的神经符号框架，用于从RDF（资源描述框架）生成文本。在这个框架中，模型是通过多个大型语言模型代理之间的协作交互来“训练”的，而不是传统的反向传播方法。这些LLM代理仅基于RDF三元组生成基于规则的Python代码，而不需要特定领域的参考文本。结果系统完全可解释，无需监督学习数据，并且使用单个CPU几乎可以即时生成文本。我们在WebNLG和OpenDialKG数据集上的实验表明，与经过微调或提示的语言模型相比，我们的方法产生的输出减少了幻觉（即不准确的或虚构的信息），仅在流畅性方面略有牺牲。
URL
https://arxiv.org/abs/2512.18360
