An Empirical Study of Recent Large Language Models for Knowledge Graph to Text Generation on Standard Benchmarks
Paper recorded by Signals 4 on 2026-10-22 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-10-22 on arXiv · recorded by Signals 4 on 2026-10-23
Category: cs.CL · 自然语言处理 · first seen 2026-10-23
Abstract
Knowledge graph-to-text (KG-to-text) generation verbalizes a set of knowledge graph triples into fluent natural language. While fine-tuned encoder-decoder models have long been the standard approach, recent LLMs are becoming increasingly competitive. We benchmark recent LLMs against fine-tuned encoder-decoder models, using reference-based metrics and graph-grounded reference-free LLM-based evaluat
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Cite this page: An Empirical Study of Recent Large Language Models for Knowledge Graph to Text Generation on Standard Benchmarks: the #167 most recent of 660 cs.CL papers we have recorded (as of 2026-10-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/an-empirical-study-of-recent-large-language-models-for-knowledge-graph-to-text-g.html
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