Sparse retrieval methods like BM25 are based on lexical overlap, focusing on the surface form of the terms that appear in the query and the document. The use of inverted indices in these methods leads to high retrieval efficiency. On the other hand, dense retrieval methods are based on learned dense vectors and, consequently, are effective but comparatively slow. Since sparse and dense methods approach problems differently and use complementary relevance signals, approximation methods were proposed to balance effectiveness and efficiency. For efficiency, approximation methods like HNSW are frequently used to approximate exhaustive dense retrieval. However, approximation techniques still exhibit considerably higher latency than sparse approaches. We propose LexBoost that first builds a network of dense neighbors (a corpus graph) using a dense retrieval approach while indexing. Then, during retrieval, we consider both a document's lexical relevance scores and its neighbors' scores to rank the documents. In LexBoost this remarkably simple application of the Cluster Hypothesis contributes to stronger ranking effectiveness while contributing little computational overhead (since the corpus graph is constructed offline). The method is robust across the number of neighbors considered, various fusion parameters for determining the scores, and different dataset construction methods. We also show that re-ranking on top of LexBoost outperforms traditional dense re-ranking and leads to results comparable with higher-latency exhaustive dense retrieval.

Documents come in all shapes and sizes and are created by many different means, including now-a-days, generative language models. We demonstrate that a simple genetic algorithm can improve generative information retrieval by using a document's text as a genetic representation, a relevance model as a fitness function, and a large language model as a genetic operator that introduces diversity through random changes to the text to produce new documents. By "mutating" highly-relevant documents and "crossing over" content between documents, we produce new documents of greater relevance to a user's information need --- validated in terms of estimated relevance scores from various models and via a preliminary human evaluation. We also identify challenges that demand further study.

Retrieval approaches that score documents based on learned dense vectors (i.e., dense retrieval) rather than lexical signals (i.e., conventional retrieval) are increasingly popular. Their ability to identify related documents that do not necessarily contain the same terms as those appearing in the user's query (thereby improving recall) is one of their key advantages. However, to actually achieve these gains, dense retrieval approaches typically require an exhaustive search over the document collection, making them considerably more expensive at query-time than conventional lexical approaches. Several techniques aim to reduce this computational overhead by approximating the results of a full dense retriever. Although these approaches reasonably approximate the top results, they suffer in terms of recall -- one of the key advantages of dense retrieval. We introduce 'LADR' (Lexically-Accelerated Dense Retrieval), a simple-yet-effective approach that improves the efficiency of existing dense retrieval models without compromising on retrieval effectiveness. LADR uses lexical retrieval techniques to seed a dense retrieval exploration that uses a document proximity graph. Through extensive experiments, we find that LADR establishes a new dense retrieval effectiveness-efficiency Pareto frontier among approximate k nearest neighbor techniques. When tuned to take around 8ms per query in retrieval latency on our hardware, LADR consistently achieves both precision and recall that are on par with an exhaustive search on standard benchmarks. Importantly, LADR accomplishes this using only a single CPU -- no hardware accelerators such as GPUs -- which reduces the deployment cost of dense retrieval systems.