# Neural Models for Information Retrieval

arXiv: https://arxiv.org/abs/1705.01509 · HTML: https://mlsanity.com/paper/1705.01509/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2017.

## Built on by (most cited first, 29 of 29)

- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
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- [Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community](https://mlsanity.com/paper/2307.09751/) (2023)
- [Beyond pip install: Evaluating LLM Agents for the Automated Installation of Python Projects](https://mlsanity.com/paper/2412.06294/) (2024)
- [Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges](https://mlsanity.com/paper/2403.00784/) (2024)
- [AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval](https://mlsanity.com/paper/2310.01880/) (2023)
- [Enhancing Tool Retrieval with Iterative Feedback from Large Language Models](https://mlsanity.com/paper/2406.17465/) (2024)
- [Diversity driven Query Rewriting in Search Advertising](https://mlsanity.com/paper/2106.03816/) (2021)
- [Learning Job Titles Similarity from Noisy Skill Labels](https://mlsanity.com/paper/2207.00494/) (2022)
- [Reliable Confidence Intervals for Information Retrieval Evaluation Using Generative A.I](https://mlsanity.com/paper/2407.02464/) (2024)
- [Large Reasoning Embedding Models: Towards Next-Generation Dense Retrieval Paradigm](https://mlsanity.com/paper/2510.14321/) (2025)
- [SPENCER: Self-Adaptive Model Distillation for Efficient Code Retrieval](https://mlsanity.com/paper/2508.00546/) (2025)
- [Artefact Retrieval: Overview of NLP Models with Knowledge Base Access](https://mlsanity.com/paper/2201.09651/) (2022)
- [Think-to-Personalize: Unifying Reasoning and Retrieval for User-Centric Personalized Dense Retrieval](https://mlsanity.com/paper/2608.18855/) (2026)
- [Embracing Structure in Data for Billion-Scale Semantic Product Search](https://mlsanity.com/paper/2110.06125/) (2021)
- [Uncovering the Limitations of Query Performance Prediction: Failures, Insights, and Implications for Selective Query Processing](https://mlsanity.com/paper/2504.01101/) (2025)
- [Overcoming Prior Misspecification in Online Learning to Rank](https://mlsanity.com/paper/2301.10651/) (2023)
- [Towards Trustworthy Reranking: A Simple yet Effective Abstention Mechanism](https://mlsanity.com/paper/2402.12997/) (2024)
- [Less is More: Compact Clue Selection for Efficient Retrieval-Augmented Generation Reasoning](https://mlsanity.com/paper/2502.11811/) (2025)
- [Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker](https://mlsanity.com/paper/2511.07969/) (2025)
- [Unbiased Sentence Encoder For Large-Scale Multi-lingual Search Engines](https://mlsanity.com/paper/2106.07719/) (2021)
- [IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scale](https://mlsanity.com/paper/2301.00716/) (2023)
- [Meta-Learning Siamese Network for Few-Shot Text Classification](https://mlsanity.com/paper/2302.03507/) (2023)
- [Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning](https://mlsanity.com/paper/2307.09177/) (2023)
- [GOLFer: Smaller LM-Generated Documents Hallucination Filter & Combiner for Query Expansion in Information Retrieval](https://mlsanity.com/paper/2506.04762/) (2025)
- [Data Kernel Perspective Space Performance Guarantees for Synthetic Data from Transformer Models](https://mlsanity.com/paper/2602.05106/) (2026)
- [Led to Mislead: Adversarial Content Injection for Attacks on Neural Ranking Models](https://mlsanity.com/paper/2605.01591/) (2026)
- [GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models](https://mlsanity.com/paper/2607.24764/) (2026)

## Abstract (arXiv)

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to rank search results in response to a query. Traditional learning to rank models employ machine learning techniques over hand-crafted IR features. By contrast, neural models learn representations of language from raw text that can bridge the gap between query and document vocabulary. Unlike classical IR models, these new machine learning based approaches are data-hungry, requiring large scale training data before they can be deployed. This tutorial introduces basic concepts and intuitions behind neural IR models, and places them in the context of traditional retrieval models. We begin by introducing fundamental concepts of IR and different neural and non-neural approaches to learning vector representations of text. We then review shallow neural IR methods that employ pre-trained neural term embeddings without learning the IR task end-to-end. We introduce deep neural networks next, discussing popular deep architectures. Finally, we review the current DNN models for information retrieval. We conclude with a discussion on potential future directions for neural IR.