A broad evaluation of twelve LLMs on five data-to-text datasets shows Llama 2 leads on factual consistency, larger size usually helps, and reference divergence hurts scores.
Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation
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abstract
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.
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An Extensive Evaluation of Factual Consistency in Large Language Models for Data-to-Text Generation
A broad evaluation of twelve LLMs on five data-to-text datasets shows Llama 2 leads on factual consistency, larger size usually helps, and reference divergence hurts scores.