mlsanityData to 14 Oct 2026

Zero-Indexing Internet Search Augmented Generation for Large Language Models

arXiv 2411.19478 ↗First posted 29 Nov 2024cs.IRcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Retrieval augmented generation has emerged as an effective method to enhance large language model performance. This approach typically relies on an internal retrieval module that uses various indexing mechanisms to manage a static pre-processed corpus. However, such a paradigm often falls short when it is necessary to integrate the most up-to-date information that has not been updated into the corpus during generative inference time. In this paper, we explore an alternative approach that leverages standard search engine APIs to dynamically integrate the latest online information (without maintaining any index for any fixed corpus), thereby improving the quality of generated content. We design a collaborative LLM-based paradigm, where we include: (i) a parser-LLM that determines if the Internet augmented generation is demanded and extracts the search keywords if so with a single inference; (ii) a mixed ranking strategy that re-ranks the retrieved HTML files to eliminate bias introduced from the search engine API; and (iii) an extractor-LLM that can accurately and efficiently extract relevant information from the fresh content in each HTML file. We conduct extensive empirical studies to evaluate the performance of this Internet search augmented generation paradigm. The experimental results demonstrate that our method generates content with significantly improved quality. Our system has been successfully deployed in a production environment to serve 01.AI's generative inference requests.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 29 of its 53 references that are in the map. First posted on arXiv 29 Nov 2024.

Builds on

29 of 53 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models15 Mar
  2. 2024Retrieval-Augmented Generation for AI-Generated Content: A Survey29 Feb

Other references · 27

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  3. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  4. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  5. 2021WebGPT: Browser-assisted question-answering with human feedbackcited by 1,745
  6. 2024RULER: What's the Real Context Size of Your Long-Context Language Models?cited by 1,346
  7. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  8. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  9. 2024Yi: Open Foundation Models by 01.AIcited by 719
  10. 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 472
  11. 2024Multilingual E5 Text Embeddings: A Technical Reportcited by 372
  12. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
15 more
  1. 2021Internet-Augmented Dialogue Generationcited by 217
  2. 2023Query Rewriting for Retrieval-Augmented Large Language Modelscited by 213
  3. 2024RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMscited by 202
  4. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 147
  5. 2022Internet-augmented language models through few-shot prompting for open-domain question answeringcited by 131
  6. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 127
  7. 2023Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learningcited by 69
  8. 2023Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preferencecited by 38
  9. 2024Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?cited by 30
  10. 2023Pre-trained Embeddings for Entity Resolution: An Experimental Analysis [Experiment, Analysis & Benchmark]cited by 13
  11. 2023Experimental Analysis of Large-scale Learnable Vector Storage Compressioncited by 12
  12. 2022LIDER: An Efficient High-dimensional Learned Index for Large-scale Dense Passage Retrievalcited by 8
  13. 2023The Web Can Be Your Oyster for Improving Large Language Modelscited by 7
  14. 2023Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloadscited by 6
  15. 2024WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphscited by 6

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