  ExaRanker recently introduced an approach to training information retrieval
(IR) models, incorporating natural language explanations as additional labels.
The method addresses the challenge of limited labeled examples, leading to
improvements in the effectiveness of IR models. However, the initial results
were based on proprietary language models such as GPT-3.5, which posed
constraints on dataset size due to its cost and data privacy. In this paper, we
introduce ExaRanker-Open, where we adapt and explore the use of open-source
language models to generate explanations. The method has been tested using
different LLMs and datasets sizes to better comprehend the effective
contribution of data augmentation. Our findings reveal that incorporating
explanations consistently enhances neural rankers, with benefits escalating as
the LLM size increases. Notably, the data augmentation method proves
advantageous even with large datasets, as evidenced by ExaRanker surpassing the
target baseline by 0.6 nDCG@10 points in our study. To encourage further
advancements by the research community, we have open-sourced both the code and
datasets at https://github.com/unicamp-dl/ExaRanker.
