We describe a reproduction of a human annotation experiment that was performed to evaluate the effectiveness of text style transfer systems (Reif et al., 2021). Despite our efforts to closely imitate the conditions of the original study, the results obtained differ significantly from those in the original study. We performed a statistical analysis of the results obtained, discussed the sources of these discrepancies in the study design, and quantified reproducibility. The reproduction followed the common approach to reproduction adopted by the ReproHum project.

Table-to-text generation (insight generation from tables) is a challenging task that requires precision in analyzing the data. In addition, the evaluation of existing benchmarks is affected by contamination of Large Language Model (LLM) training data as well as domain imbalance. We introduce FreshTab, an on-the-fly table-to-text benchmark generation from Wikipedia, to combat the LLM data contamination problem and enable domain-sensitive evaluation. While non-English table-to-text datasets are limited, FreshTab collects datasets in different languages on demand (we experiment with German, Russian and French in addition to English). We find that insights generated by LLMs from recent tables collected by our method appear clearly worse by automatic metrics, but this does not translate into LLM and human evaluations. Domain effects are visible in all evaluations, showing that a domain-balanced benchmark is more challenging.