Application of LLMs for complex causal question answering can be stymied by their opacity and propensity for hallucination. Although recent approaches such as Retrieval Augmented Generation and Chain of Thought prompting have improved reliability, we argue current approaches are insufficient and further fail to satisfy key criteria humans use to select and evaluate causal explanations. Inspired by findings from the social sciences, we present an implemented causal QA approach that combines iterative RAG with guidance from a formal model of causation. Our causal model is backed by the Cogent reasoning engine, allowing users to interactively perform counterfactual analysis and refine their answer. Our approach has been integrated into a deployed Collaborative Research Assistant (Cora) and we present a pilot evaluation in the life sciences domain.

We propose a method for online news stream clustering that is a variant of the non-parametric streaming K-means algorithm. Our model uses a combination of sparse and dense document representations, aggregates document-cluster similarity along these multiple representations and makes the clustering decision using a neural classifier. The weighted document-cluster similarity model is learned using a novel adaptation of the triplet loss into a linear classification objective. We show that the use of a suitable fine-tuning objective and external knowledge in pre-trained transformer models yields significant improvements in the effectiveness of contextual embeddings for clustering. Our model achieves a new state-of-the-art on a standard stream clustering dataset of English documents.