  Predicting chemical reactions, a fundamental challenge in chemistry, involves
forecasting the resulting products from a given reaction process. Conventional
techniques, notably those employing Graph Neural Networks (GNNs), are often
limited by insufficient training data and their inability to utilize textual
information, undermining their applicability in real-world applications. In
this work, we propose ReLM, a novel framework that leverages the chemical
knowledge encoded in language models (LMs) to assist GNNs, thereby enhancing
the accuracy of real-world chemical reaction predictions. To further enhance
the model's robustness and interpretability, we incorporate the confidence
score strategy, enabling the LMs to self-assess the reliability of their
predictions. Our experimental results demonstrate that ReLM improves the
performance of state-of-the-art GNN-based methods across various chemical
reaction datasets, especially in out-of-distribution settings. Codes are
available at https://github.com/syr-cn/ReLM.
