Advancements in retrieval models necessitate re-indexing, a computationally expensive process for large-scale production environments. While updating only the query encoder and continuing to use the old index could be a promising middle ground, dense retrieval systems suffer severe performance drops in this setting. We investigate whether Learned Sparse Retrieval (LSR) can mitigate this backward compatibility issue, as its lexical matching may provide a stable term-based anchor to preserve compatibility across model versions. Experiments on BEIR and the streaming settings of LoTTE show that upgrading only the query encoder causes only a small effectiveness drop in LSR when no mitigation applied, whereas dense retrieval fails severely. We explore lightweight query adaptation methods including ranking fusion, representation fusion, and minimal-training adapters to further improve compatibility. These approaches significantly improve backward compatibility on BEIR and effectively reduce performance loss in streaming retrieval. Code: https://github.com/JingfenQiao/LSR-BC.git

We present AnnoRank, a web-based user interface (UI) framework designed to facilitate collecting crowdsource annotations in the context of information retrieval. AnnoRank enables the collection of explicit and implicit annotations for a specified query and a single or multiple documents, allowing for the observation of user-selected items and the assignment of relevance judgments. Furthermore, AnnoRank allows for ranking comparisons, allowing for the visualization and evaluation of a ranked list generated by different fairness interventions, along with its utility and fairness metrics. Fairness interventions in the annotation pipeline are necessary to prevent the propagation of bias when a user selects the top-k items in a ranked list. With the widespread use of ranking systems, the application supports multimodality through text and image document formats. We also support the assessment of agreement between annotators to ensure the quality of the annotations. AnnoRank is integrated with the Ranklib library, offering a vast range of ranking models that can be applied to the data and displayed in the UI. AnnoRank is designed to be flexible, configurable, and easy to deploy to meet diverse annotation needs in information retrieval. AnnoRank is publicly available as open-source software, together with detailed documentation at https://github.com/ClaraRus/AnnoRank.