# Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering Source: [Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:31:00.251Z Authors: Sebastian Hofst\\"atter, Markus Zlabinger, Mete Sertkan, Michael Schr\\"oder, Allan Hanbury Published: 2020-08-12T14:59:50.000Z ## Abstract There are many existing retrieval and question answering datasets. However, most of them either focus on ranked list evaluation or single-candidate question answering. This divide makes it challenging to properly evaluate approaches concerned with ranking documents and providing snippets or answers for a given query. In this work, we present FiRA: a novel dataset of Fine-Grained Relevance Annotations. We extend the ranked retrieval annotations of the Deep Learning track of TREC 2019 with passage and word level graded relevance annotations for all relevant documents. We use our newly created data to study the distribution of relevance in long documents, as well as the attention of annotators to specific positions of the text. As an example, we evaluate the recently introduced TKL document ranking model. We find that although TKL exhibits state-of-the-art retrieval results for long documents, it misses many relevant passages. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()