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Large Language Models (LLMs) are widely used in critical fields such as healthcare, education, and finance due to their remarkable proficiency in various language-related tasks. However, LLMs are prone to generating factually incorrect responses or "hallucinations," which can lead to a loss of credibility and trust among users. To address this issue, we propose a multi-stage framework that generates the rationale first, verifies and refines incorrect ones, and uses them as supporting references to generate the answer. The generated rationale enhances the transparency of the answer and our framework provides insights into how the model arrived at this answer, by using this rationale and the references to the context. In this paper, we demonstrate its effectiveness in improving the quality of responses to drug-related inquiries in the life sciences industry. Our framework improves traditional Retrieval Augmented Generation (RAG) by enabling OpenAI GPT-3.5-turbo to be 14-25% more faithful and 16-22% more accurate on two datasets. Furthermore, fine-tuning samples based on our framework improves the accuracy of smaller open-access LLMs by 33-42% and competes with RAG on commercial models.

Molecular property prediction is a critical task in computational drug discovery. While recent advances in Graph Neural Networks (GNNs) and Transformers have shown to be effective and promising, they face the following limitations: Transformer self-attention does not explicitly consider the underlying molecule structure while GNN feature representation alone is not sufficient to capture granular and hidden interactions and characteristics that distinguish similar molecules. To address these limitations, we propose SYN- FUSION, a novel approach that synergistically combines pre-trained features from GNNs and Transformers. This approach provides a comprehensive molecular representation, capturing both the global molecule structure and the individual atom characteristics. Experimental results on MoleculeNet benchmarks demonstrate superior performance, surpassing previous models in 5 out of 7 classification datasets and 4 out of 6 regression datasets. The performance of SYN-FUSION has been compared with other Graph-Transformer models that have been jointly trained using a combination of transformer and graph features, and it is found that our approach is on par with those models in terms of performance. Extensive analysis of the learned fusion model across aspects such as loss, latent space, and weight distribution further validates the effectiveness of SYN-FUSION. Finally, an ablation study unequivocally demonstrates that the synergy achieved by SYN-FUSION surpasses the performance of its individual model components and their ensemble, offering a substantial improvement in predicting molecular properties.