With the advent to deep learning models, there is a lot of new work to be done in the area of information retrieval (IR). See below for examples.

• Azad & Deepak 2017 - Query Expansion Techniques for Information Retrieval: A Survey

• Yu et al 2021 - PGT: Pseudo Relevance Feedback Using a Graph-Based Transformer
• Naseri et al 2021 - CEQE: Contextualized Embeddings for Query Expansion
• Gao et al 2021 - COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List There is really a lot that can be done in this area. This is just the beginning

See Conversational Search.

These are retrieval methods used in NLP tasks such as open-domain QA, retrieval-based dialog, retrieval-based prompting, etc.

• BM25
  • Trotman et al 2014 - Improvements to BM25 and Language Models Examined Github implementation of various variations
• Generating Titles
  • Chen et al 2023 - GERE: Generative Evidence Retrieval for Fact Verification

• Overviews
  • Zhao et al 2023 - Dense Text Retrieval based on Pretrained Language Models: A Survey
• Ma et al 2023 - A Contrastive Pre-training Approach to Learn Discriminative Autoencoder for Dense Retrieval

• Overviews
  • Li et al 2024 - From Matching to Generation: A Survey on Generative Information Retrieval

See also here and here.

• Yahoo! Learning to Rank Challenge homepage
• Thakur et al 2021 - BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

• Lemur Open-source research search engine
• PeARS Local browser-based search engine (seems to be a research system)
• Rank-BM25 A two line search engine

• SIGIR, 2019, 2020, 2021

• James Allan
• Jamie Callan (not up-to-date) or arXiv
• Bruce Croft
• Laura Dietz
• Ji-Rong Wen

• Conversational Search
• Learning to Rank