mlsanityData to 7 Oct 2026

Large Search Model: Redefining Search Stack in the Era of LLMs

arXiv 2310.14587 ↗First posted 23 Oct 2023cs.IR cs.CLcited by 18

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Abstract (arXiv)

Modern search engines are built on a stack of different components, including query understanding, retrieval, multi-stage ranking, and question answering, among others. These components are often optimized and deployed independently. In this paper, we introduce a novel conceptual framework called large search model, which redefines the conventional search stack by unifying search tasks with one large language model (LLM). All tasks are formulated as autoregressive text generation problems, allowing for the customization of tasks through the use of natural language prompts. This proposed framework capitalizes on the strong language understanding and reasoning capabilities of LLMs, offering the potential to enhance search result quality while simultaneously simplifying the existing cumbersome search stack. To substantiate the feasibility of this framework, we present a series of proof-of-concept experiments and discuss the potential challenges associated with implementing this approach within real-world search systems.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 49 of its 53 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

49 of 53 references.

References · 49

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37 more
  1. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 3; 2026: 5.

18 papers.

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  1. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  2. 2024Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankerscited by 42
  3. 2024BatchLLM: Optimizing Large Batched LLM Inference with Global Prefix Sharing and Throughput-oriented Token Batchingcited by 31
  4. 2025vCache: Verified Semantic Prompt Cachingcited by 15
  5. 2024Web Retrieval Agents for Evidence-Based Misinformation Detectioncited by 13
  6. 2024Large Language Models for Social Networks: Applications, Challenges, and Solutionscited by 11
  7. 2024STAR: A Simple Training-free Approach for Recommendations using Large Language Modelscited by 8
  8. 2024UniICL: An Efficient Unified Framework Unifying Compression, Selection, and Generationcited by 6
  9. 2025A Systematic Framework for Enterprise Knowledge Retrieval: Leveraging LLM-Generated Metadata to Enhance RAG Systemscited by 3
  10. 2026Asynchronous Verified Semantic Caching for Tiered LLM Architecturescited by 3
  11. 2024Enhancing Exploratory Learning through Exploratory Search with the Emergence of Large Language Modelscited by 2
  12. 2024Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Eventscited by 1
6 more
  1. 2026Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoderscited by 1
  2. 2024Structured Language Generation Model: Loss Calibration and Formatted Decoding for Robust Structure Prediction and Knowledge Retrievalcited by 0
  3. 2025How good are LLMs at Retrieving Documents in a Specific Domain?cited by 0
  4. 2026HyFunc: Accelerating LLM-based Function Calls for Agentic AI through Hybrid-Model Cascade and Dynamic Templatingcited by 0
  5. 2026Verify as You Go: An LLM-Powered Browser Extension for Fake News Detectioncited by 0
  6. 2026ResRank: Unifying Retrieval and Listwise Reranking via End-to-End Joint Training with Residual Passage Compressioncited by 0

Similar, not cited

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  1. 2024Enhancing Cloud-Based Large Language Model Processing with Elasticsearch and Transformer Models
  2. 2024A Survey of Query Optimization in Large Language Models
  3. 2026Aligning Large Language Models with Searcher Preferences
  4. 2026Over-Searching in Search-Augmented Large Language Models
  5. 2024Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models
  6. 2022QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation
  7. 2023Query Rewriting for Retrieval-Augmented Large Language Models
  8. 2024Zero-Indexing Internet Search Augmented Generation for Large Language Models