对话式大语言模型在基于知识的文本生成中的比较分析
A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation
AI总结：
本研究针对对话式大语言模型易产生幻觉等问题，对比4款不同规模模型在不同提示技术下基于语义三元组生成文本的表现，发现少样本提示等技术可显著提升其三元组表述能力，小模型提升效果更明显。
摘要：
从图结构数据生成自然语言文本对于对话式信息检索至关重要。源自知识图谱的语义三元组能够为对话智能体的回复提供事实依据，是其信息输出的重要支撑来源。这一点在大语言模型语境下尤为关键——这类模型在对话交互上潜力巨大，但容易产生幻觉、遗漏信息或输出矛盾内容。\n本研究针对对话式大语言模型基于语义三元组生成自然语言文本的能力开展实证分析，对比了4款不同规模的大语言模型在不同提示技术下的表现。通过在WebNLG数据集上开展的一系列基准实验，我们分析了各模型的性能，并明确了生成预测结果中最常见的问题。研究结果表明，通过少样本提示、后处理以及高效微调技术，大语言模型的三元组表述能力可得到显著提升，尤其适用于零样本表现较差的小尺寸模型。
英文摘要：
Generating natural language text from graph-structured data is essential for conversational information seeking. Semantic triples derived from knowledge graphs can serve as a valuable source for grounding responses from conversational agents by providing a factual basis for the information they communicate. This is especially relevant in the context of large language models, which offer great potential for conversational interaction but are prone to hallucinating, omitting, or producing conflicting information. In this study, we conduct an empirical analysis of conversational large language models in generating natural language text from semantic triples. We compare four large language models of varying sizes with different prompting techniques. Through a series of benchmark experiments on the WebNLG dataset, we analyze the models' performance and identify the most common issues in the generated predictions. Our findings show that the capabilities of large language models in triple verbalization can be significantly improved through few-shot prompting, post-processing, and efficient fine-tuning techniques, particularly for smaller models that exhibit lower zero-shot performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
