  Generating user-friendly explanations regarding why an item is recommended
has become increasingly common, largely due to advances in language generation
technology, which can enhance user trust and facilitate more informed
decision-making when using online services. However, existing explainable
recommendation systems focus on using small-size language models. It remains
uncertain what impact replacing the explanation generator with the recently
emerging large language models (LLMs) would have. Can we expect unprecedented
results?
  In this study, we propose LLMXRec, a simple yet effective two-stage
explainable recommendation framework aimed at further boosting the explanation
quality by employing LLMs. Unlike most existing LLM-based recommendation works,
a key characteristic of LLMXRec is its emphasis on the close collaboration
between previous recommender models and LLM-based explanation generators.
Specifically, by adopting several key fine-tuning techniques, including
parameter-efficient instructing tuning and personalized prompt techniques,
controllable and fluent explanations can be well generated to achieve the goal
of explanation recommendation. Most notably, we provide three different
perspectives to evaluate the effectiveness of the explanations. Finally, we
conduct extensive experiments over several benchmark recommender models and
publicly available datasets. The experimental results not only yield positive
results in terms of effectiveness and efficiency but also uncover some
previously unknown outcomes. To facilitate further explorations in this area,
the full code and detailed original results are open-sourced at
https://github.com/GodFire66666/LLM_rec_explanation/.
