In this work, we discuss a recently popular type of recommender system: an LLM-based coding assistant. Connecting the task of providing code recommendations in multiple formats to traditional RecSys challenges, we outline several similarities and differences due to domain specifics. We emphasize the importance of providing relevant context to an LLM for this use case and discuss lessons learned from context enhancements & offline and online evaluation of such AI-assisted coding systems.

We demonstrate, via competitive bag-of-words first-stage retrieval baselines for the MS MARCO document ranking task, seamless replicability and interoperability between Elasticsearch and the Pyserini IR toolkit, which are both built on the open-source Lucene search library. This integration highlights the benefits of recent efforts to promote the use of Lucene in information retrieval research to better align the research and practice of building search applications. Closer alignment between academia and industry is mutually beneficial: Academic researchers gain a smoother path to real-world impact because their contributions can be more easily deployed in production applications. Industry practitioners gain an easy way to benchmark their innovations in a rigorous and vendor-neutral manner by exploiting evaluation resources and infrastructure built by the academic community. This two-way exchange between academia and industry allows both parties to "have their cakes and eat them too".

Broad-coverage relation extraction either requires expensive supervised training data, or suffers from drawbacks inherent to distant supervision.We present an approach for providing partial supervision to a distantly supervised relation extractor using a small number of carefully selected examples.We compare against established active learning criteria and propose a novel criterion to sample examples which are both uncertain and representative.In this way, we combine the benefits of fine-grained supervision for difficult examples with the coverage of a large distantly supervised corpus.Our approach gives a substantial increase of 3.9% endto-end F 1 on the 2013 KBP Slot Filling evaluation, yielding a net F 1 of 37.7%.