  Data-to-text (D2T) generation aims to generate human-readable text from
semi-structured data, such as tables and graphs. The recent success of D2T is
largely attributed to advancements in LLMs. Despite the success of LLMs, no
research has been conducted to illustrate the impact of model size on the
performance of fine-tuned LLMs for D2T tasks. D2T model performance is
typically assessed based on three key qualities: \textit{readability}
(indicates fluency and coherence), \textit{informativeness} (measures content
similarity), and \textit{faithfulness} (assesses consistency of factual
information). It is currently uncertain whether increasing the size of LLMs
effectively improves performance in D2T tasks across these three qualities. The
objective of this study is to investigate the performance of fine-tuned LLMs in
D2T tasks in terms of model size. Through extensive comparative analysis, we
aim to elucidate both the advantages and limitations of scaling model sizes
across five widely used D2T datasets (E2E, ViGGo, WikiTableText, DART, and
WebNLG) and twelve state-of-the-art LLMs with varying sizes from five different
LLM families (T5, BART, OPT, BLOOM, and Llama 2). To comprehensively cover all
the three essential qualities of D2T models, we incorporate six widely
recognized automatic metrics -- \textsc{BLEU}, \textsc{METEOR},
\textsc{BERTScore}, \textsc{MoverScore}, \textsc{Parent}, and
\textsc{BARTScore}. We also provide an in-depth analysis of LLM performance
concerning model size in the presence of source-reference divergence, a
critical aspect of D2T tasks. Our investigation reveals that increasing LLM
size enhances \textit{readability} and \textit{informativeness} in D2T tasks,
but larger (in terms of size) LLMs may sacrifice \textit{faithfulness}.
Moreover, small-sized LLMs show more resilience than larger ones when
source-reference divergence is present.
