A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation
Phillip Schneider, Manuel Klettner, Elena Simperl, Florian Matthes
Abstract
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.- Anthology ID:
- 2024.eacl-short.31
- Volume:
- Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 358–367
- Language:
- URL:
- https://aclanthology.org/2024.eacl-short.31
- DOI:
- Cite (ACL):
- Phillip Schneider, Manuel Klettner, Elena Simperl, and Florian Matthes. 2024. A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 358–367, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation (Schneider et al., EACL 2024)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2024.eacl-short.31.pdf