用多个LLM代理协作生成可解释的RDF转文本规则代码
LLM Agents Implement an NLG System from Scratch: Building Interpretable Rule-Based RDF-to-Text Generators
- 通过多LLM代理协作生成规则代码，无需传统训练
- 在WebNLG和OpenDialKG上减少幻觉，仅轻微影响流畅性
- 适合需要可解释性和低延迟的领域应用
我们提出一种新型神经符号框架用于RDF-to-text生成，模型通过多个LLM代理间的协作互动进行‘训练’，而非传统反向传播。这些代理仅基于RDF三元组生成目标领域的规则化Python代码，无需领域内人类参考文本。所生成系统完全可解释，无需监督训练数据，且仅需单个CPU即可近实时生成文本。在WebNLG和OpenDialKG数据集上的实验表明，该方法生成内容幻觉显著减少，与微调或提示语言模型相比，仅产生轻微流畅性损失。
原文摘要 · Abstract (English)
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
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