We analyze the behaviors of open large language models (LLMs) on the task of data-to-text (D2T) generation, i.e., generating coherent and relevant text from structured data. To avoid the issue of LLM training data contamination with standard benchmarks, we design Quintd - a tool for collecting novel structured data records from public APIs. We find that open LLMs (Llama 2, Mistral, and Zephyr) can generate fluent and coherent texts in zero-shot settings from data in common formats collected with Quintd. However, we show that the semantic accuracy of the outputs is a major issue: both according to human annotators and our reference-free metric based on GPT-4, more than 80% of the outputs of open LLMs contain at least one semantic error. We publicly release the code, data, and model outputs.
@article{arxiv.2401.10186,
title = {Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation},
author = {Zdeněk Kasner and Ondřej Dušek},
journal= {arXiv preprint arXiv:2401.10186},
year = {2024}
}