Recent advancements in dense neural retrievers and language models have led to large improvements in state-of-the-art approaches to open-domain Question Answering (QA) based on retriever-reader architectures. However, issues stemming from data quality and imbalances in the use of dense embeddings have hindered performance, particularly for less common entities and facts. To tackle these problems, this study explores a multi-modal passage retrieval model’s potential to bolster QA system performance. This study poses three key questions: (1) Can a distantly supervised question-relation extraction model enhance retrieval using a knowledge graph (KG), compensating for dense neural retrievers’ shortcomings with rare entities? (2) How does this multi-modal approach compare to existing QA systems based on textual features? (3) Can this QA system alleviate poor performance on less common entities on common benchmarks? We devise a multi-modal retriever combining entity features and textual data, leading to improved retrieval precision in some situations, particularly for less common entities. Experiments across different datasets confirm enhanced performance for entity-centric questions, but challenges remain in handling complex generalized questions.

Structured knowledge bases (KBs) are an asset for search engines and other applications but are inevitably incomplete. Language models (LMs) have been proposed for unsupervised knowledge base completion (KBC), yet, their ability to do this at scale and with high accuracy remains an open question. Prior experimental studies mostly fall short because they only evaluate on popular subjects, or sample already existing facts from KBs. In this work, we perform a careful evaluation of GPT’s potential to complete the largest public KB: Wikidata. We find that, despite their size and capabilities, models like GPT-3, ChatGPT and GPT-4 do not achieve fully convincing results on this task. Nonetheless, it provides solid improvements over earlier approaches with smaller LMs. In particular, we show that it is feasible to extend Wikidata by 27M facts at 90% precision.