# Information Retrieval: Recent Advances and Beyond

arXiv: https://arxiv.org/abs/2301.08801 · HTML: https://mlsanity.com/paper/2301.08801/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2023.

## Built on by (most cited first, 30 of 49)

- [Benchmarking Prompt Sensitivity in Large Language Models](https://mlsanity.com/paper/2502.06065/) (2025)
- [RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing](https://mlsanity.com/paper/2404.19543/) (2024)
- [MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion](https://mlsanity.com/paper/2310.19056/) (2023)
- [Question-Based Retrieval using Atomic Units for Enterprise RAG](https://mlsanity.com/paper/2405.12363/) (2024)
- [Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG](https://mlsanity.com/paper/2409.07691/) (2024)
- [Writing Style Matters: An Examination of Bias and Fairness in Information Retrieval Systems](https://mlsanity.com/paper/2411.13173/) (2024)
- [Interpretable Text Embeddings and Text Similarity Explanation: A Survey](https://mlsanity.com/paper/2502.14862/) (2025)
- [Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers](https://mlsanity.com/paper/2505.20128/) (2025)
- [Keyword search is all you need: Achieving RAG-Level Performance without vector databases using agentic tool use](https://mlsanity.com/paper/2602.23368/) (2025)
- [PhysBERT: A Text Embedding Model for Physics Scientific Literature](https://mlsanity.com/paper/2408.09574/) (2024)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [From Ranking to Selection: A Simple but Efficient Dynamic Passage Selector for Retrieval Augmented Generation](https://mlsanity.com/paper/2508.09497/) (2025)
- [SynapticRAG: Enhancing Temporal Memory Retrieval in Large Language Models through Synaptic Mechanisms](https://mlsanity.com/paper/2410.13553/) (2024)
- [Bias-Aware Agent: Enhancing Fairness in AI-Driven Knowledge Retrieval](https://mlsanity.com/paper/2503.21237/) (2025)
- [LegalRAG: A Hybrid RAG System for Multilingual Legal Information Retrieval](https://mlsanity.com/paper/2504.16121/) (2025)
- [COS-Mix: Cosine Similarity and Distance Fusion for Improved Information Retrieval](https://mlsanity.com/paper/2406.00638/) (2024)
- [Smart Audit System Empowered by LLM](https://mlsanity.com/paper/2410.07677/) (2024)
- [ASSERTIFY: Utilizing Large Language Models to Generate Assertions for Production Code](https://mlsanity.com/paper/2411.16927/) (2024)
- [EcoSafeRAG: Efficient Security through Context Analysis in Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.13506/) (2025)
- [Test-time Corpus Feedback: From Retrieval to RAG](https://mlsanity.com/paper/2508.15437/) (2025)
- [NAIL: Lexical Retrieval Indices with Efficient Non-Autoregressive Decoders](https://mlsanity.com/paper/2305.14499/) (2023)
- [DS@GT at CheckThat! 2025: Evaluating Context and Tokenization Strategies for Numerical Fact Verification](https://mlsanity.com/paper/2507.06195/) (2025)
- [+VeriRel: Verification Feedback to Enhance Document Retrieval for Scientific Fact Checking](https://mlsanity.com/paper/2508.11122/) (2025)
- [Investigating LLM Variability in Personalized Conversational Information Retrieval](https://mlsanity.com/paper/2510.03795/) (2025)
- [MedAI: Evaluating TxAgent's Therapeutic Agentic Reasoning in the NeurIPS CURE-Bench Competition](https://mlsanity.com/paper/2512.11682/) (2025)
- [Enhancing Retrieval Processes for Language Generation with Augmented Queries](https://mlsanity.com/paper/2402.16874/) (2024)
- [URL: Universal Referential Knowledge Linking via Task-instructed Representation Compression](https://mlsanity.com/paper/2404.16248/) (2024)
- [MODOC: A Modular Interface for Flexible Interlinking of Text Retrieval and Text Generation Functions](https://mlsanity.com/paper/2408.14623/) (2024)
- [RAGtifier: Evaluating RAG Generation Approaches of State-of-the-Art RAG Systems for the SIGIR LiveRAG Competition](https://mlsanity.com/paper/2506.14412/) (2025)
- [SHE: Stepwise Hybrid Examination Reinforcement Learning Framework for E-commerce Search Relevance](https://mlsanity.com/paper/2510.07972/) (2025)

## Abstract (arXiv)

In this paper, we provide a detailed overview of the models used for information retrieval in the first and second stages of the typical processing chain. We discuss the current state-of-the-art models, including methods based on terms, semantic retrieval, and neural. Additionally, we delve into the key topics related to the learning process of these models. This way, this survey offers a comprehensive understanding of the field and is of interest for for researchers and practitioners entering/working in the information retrieval domain.