부산대학교 도서관

Machine learning models, particularly language models, are notoriously difficult to introspect. Black-box models can mask both issues in model training and harmful biases. For human-in-the-loop processes, opaque predictions can drive lack of trust, limiting a model's impact even when it performs effectively. To address these issues, we introduce Retrieve to Explain (R2E). R2E is a retrieval-based language model that prioritizes amongst a pre-defined set of possible answers to a research question based on the evidence in a document corpus, using Shapley values to identify the relative importance of pieces of evidence to the final prediction. R2E can adapt to new evidence without retraining, and incorporate structured data through templating into natural language. We assess on the use case of drug target identification from published scientific literature, where we show that the model outperforms an industry-standard genetics-based approach on predicting clinical trial outcomes.