Hybrid retrievers with generative re-rankers
Marek Kozłowski · Annals of Computer Science and Information Systems · 2023
The passage retrieval task was announced during PolEval 2022 (SemEval-inspired evaluation campaign for natural language processing tools for Polish).Passage retrieval is a crucial part of modern open-domain question answering systems that rely on precise and efficient retrieval components to identify passages that contain correct answers.Our solution to this task is a multi-stage neural information retrieval system.The first stage consists of a candidate passage retrieval step in which passages are retrieved using federated search over sparse (BM25) and dense indexes (two FAISS indexes built using bi-encoder type retrievers based on Polish RoBERTa models).The second stage consists of a re-ranking step of the previously selected passages with a neural model, mt5-13b-mmarco.The model scores each passage by its relevance to a given query.The highest-scoring passages are then retained as the final result.Our system achieved second place in the competition.