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When Search Engine Services meet Large Language Models: Visions and Challenges

Haoyi Xiong, Jiang Bian, Yuchen Li, Xuhong Li, Mengnan Du, Shuaiqiang Wang, Dawei Yin, Sumi Helal

arXiv:2407.00128v1cs.IRcs.AIcs.LG

TL;DR

As query complexity and expectations for precise, current responses grow, both search engines and LLMs face challenges. The paper examines their reciprocal integration and concludes that search capabilities can make LLMs more relevant, accurate, and adaptable.

  • Problem

    Growing query complexity and expectations for precise, contextually relevant, up-to-date responses challenge traditional search, while LLMs may struggle with out-of-domain queries.

    Source: §1 · p. 1, §3.4.2 · p. 7

  • Method

    The paper surveys Search4LLM approaches that use search data and ranking to improve LLMs, alongside LLM4Search approaches for summarization, indexing, query optimization, relevance ranking, and annotation.

    Source: §1 · p. 2

  • Results

    The paper's synthesis presents search integration as improving LLM accuracy and relevance, especially for real-time, domain-specific, and out-of-distribution queries, while supporting adaptability and up-to-date information.

    Source: §3.5 · p. 8

  • Takeaways & Limitations

    The paper's vision is to incorporate search engine capabilities throughout the LLM life cycle, from pretraining and fine-tuning to alignment and applications.

    Source: §3.5 · p. 8

    Agents supporting Search4LLM and LLM4Search still need sophisticated memory, including adapting it for dynamic and efficient use.

    Source: §5.3 · p. 14

Abstract

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Combining Large Language Models (LLMs) with search engine services marks a significant shift in the field of services computing, opening up new possibilities to enhance how we search for and retrieve information, understand content, and interact with internet services. This paper conducts an in-depth examination of how integrating LLMs with search engines can mutually benefit both technologies. We focus on two main areas: using search engines to improve LLMs (Search4LLM) and enhancing search engine functions using LLMs (LLM4Search). For Search4LLM, we investigate how search engines can provide diverse high-quality datasets for pre-training of LLMs, how they can use the most relevant documents to help LLMs learn to answer queries more accurately, how training LLMs with Learning-To-Rank (LTR) tasks can enhance their ability to respond with greater precision, and how incorporating recent search results can make LLM-generated content more accurate and current. In terms of LLM4Search, we examine how LLMs can be used to summarize content for better indexing by search engines, improve query outcomes through optimization, enhance the ranking of search results by analyzing document relevance, and help in annotating data for learning-to-rank tasks in various learning contexts. However, this promising integration comes with its challenges, which include addressing potential biases and ethical issues in training models, managing the computational and other costs of incorporating LLMs into search services, and continuously updating LLM training with the ever-changing web content. We discuss these challenges and chart out required research directions to address them. We also discuss broader implications for service computing, such as scalability, privacy concerns, and the need to adapt search engine architectures for these advanced models.

1 INTRODUCTION

The paper examines how search engines and LLMs can address growing demands for precise, contextually relevant, and current information. It organizes this reciprocal integration as Search4LLM and LLM4Search, while identifying technical and ethical challenges.

Motivation

  • Search engines can supply current web results to LLMs through RAG, enriching responses with information beyond pre-training and fine-tuning datasets.The introduction says this can improve accuracy and currency and help counter hallucinations.Source: §1 · p. 1

Historical context

  • The introduction traces co-evolving search and AI milestones from early foundational work through PageRank, neural retrieval, Transformers, BERT, GPT, and chat-based Bing.The milestones are presented as historical context for the integration under study.Source: §1 · p. 2, Figure 1 · p. 2

Research scope

  • The paper frames the two directions as reciprocal: search data and functions can enrich LLMs, while LLM capabilities can enhance search performance.It situates this exploration within services computing.Source: §1 · pp. 1–2

Search4LLM

  • Search4LLM uses search-engine data and ranked documents to support LLM pre-training and fine-tuning, while LTR can help models assess and prioritize relevance.The stated aim is more relevant and precise query responses.Source: §1 · p. 2

LLM4Search

  • LLM4Search applies LLMs to content summarization and indexing, query optimization, document-relevance ranking, and LTR data annotation.The paper also identifies technical demands, training biases, ethical concerns, and ongoing dataset updates as integration challenges.Source: §1 · p. 2

2 BACKGROUNDS AND PRELIMINARIES

The section introduces search-engine services and LLMs as the foundations for the paper’s research.

  • The section establishes the fundamentals of search-engine services and LLMs as groundwork for the research.Source: §2 · p. 2

2.1 Search Engine Services

The section reviews search-engine services through their architecture, data handling, retrieval and ranking, and evaluation practices.

2.1.1 Data Collection

  • Search engines collect web resources with crawlers and extract terms and phrases, using metadata and NLP techniques to improve matching between queries and documents.The collected resources include web pages, images, videos, and other multimedia.Source: §2.1.1 · p. 3

2.1.2 Storage and Indexing

  • An inverted index maps terms to documents containing them to narrow keyword searches, while TF-IDF term weighting prioritizes informative terms.Source: §2.1.2 · p. 3

2.1.3 Retrieval and Ranking

  • Retrieval and ranking process queries, score document relevance, order results, personalize them, and refine performance through continuous optimization.LTR approaches include pointwise document scoring, pairwise comparisons, and listwise ordering; training options include active, semi-supervised, and pretrain-finetuned LTR.Source: §2.1.3 · p. 3

2.1.4 Evaluation for Search Engine Services

  • A/B testing compares search-engine variants using defined objectives, randomized user segments, and performance data collected until statistical significance.Source: §2.1.4 · pp. 3–4
  • Search evaluation uses P@k and NDCG for ranking accuracy, MRR for retrieval speed, CTR and satisfaction for engagement, and conversion metrics for commercial impact.Source: §2.1.4 · p. 4

2.2 Large Language Models (LLMs)

The section outlines transformer model families, pre-training tasks, supervised fine-tuning and alignment, and LLM extensions through prompts, reasoners, and agents.

2.2.1 Foundation Models of LLMs

  • Transformers use self-attention, and the section distinguishes encoder-only BERT, decoder-only GPT, and encoder-decoder BART architectures.The model families are associated with contextual representations, text generation, and understanding and generation, respectively.Source: §2.2.1 · p. 4

2.2.2 Pre-training Models

  • Pre-training tasks include masked-language and next-token prediction, sentence-order tasks, permutation language modeling, and contrastive learning.These tasks are described as building linguistic foundations for NLP applications.Source: §2.2.2 · p. 4

2.2.3 Supervised Fine-tuning (SFT) and Alignments

  • SFT adapts pre-trained models to specific tasks, while RLHF aligns outputs with human judgments through reward modeling, policy updates, and continued feedback-based fine-tuning.The section states that combining SFT and alignment can improve performance, ethical soundness, and practical value.Source: §2.2.3 · pp. 4–5

2.2.4 LLM Extensions and Usages

  • LLM extensions include prompts for adaptable interactions, reasoning frameworks for complex problem-solving, and agents that use tools and perform tasks autonomously.The section also describes agents as having memory, planning, and collaboration capabilities.Source: §2.2.4 · p. 5

3 SEARCH4LLM: ENHANCING LLMS WITH SEARCH ENGINE SERVICES

Search4LLM frames search-engine services as support for the full LLM lifecycle, from pre-training and fine-tuning through alignment and applications. It describes how search data and functions can support broader training corpora, more relevant responses, and updated or cross-domain information.

3 SEARCH4LLM: ENHANCING LLMS WITH SEARCH ENGINE SERVICES

  • Search-engine functions can support the full LLM lifecycle, from pre-training through fine-tuning, alignment, and applications.Fig. 4 frames the approach as processing crawled web data and LLM responses into datasets for pre-training, supervised fine-tuning, and model alignment.Source: §3 · p. 5, Figure 4 · p. 6

3.1 Enhanced LLM Pre-training

  • Search engines provide varied web corpora, while domain and text-quality selection can balance training data and reduce domain bias.The described sources span languages, formats, and topics; quality indicators emphasize diversity, reliability, and authority.Source: §3.1.1 · p. 5, §3.1.2 · pp. 5–6

3.1.3 Supporting Continuous Model Improvement

  • Continuously updated search indexes can supply recent content for improving models’ currency and relevance.The paper illustrates regular updates with news and reports about unfolding events or new discoveries.Source: §3.1.3 · p. 6

3.2 Enhanced LLM Fine-tuning

  • Search-query rewrites can expose user intent, while real queries paired with relevant or clicked results can form question-answer data for supervised fine-tuning.The resulting examples reflect user phrasing and information found useful in search interactions.Source: §3.2.1 · p. 6, §3.2.2 · p. 6, Figure 5 · p. 6

3.2.3 Incorporating Domain-specific Knowledge

  • Fine-tuning on domain-relevant queries and authoritative content can build specialized knowledge and support precise answers within those domains.The examples include medicine, law, and technology.Source: §3.2.3 · pp. 6–7

3.3 Enhanced LLM Alignment

  • Search-engine ranking can prioritize relevant LLM outputs, while content screening and quality assessment can provide alignment signals.The paper describes filtering harmful content and using credibility, informativeness, and engagement assessments to guide subsequent fine-tuning.Source: §3.3 · p. 7, §3.3.1 · p. 7, §3.3.2 · p. 7

3.4 Enhanced LLM Applications

  • Retrieval-augmented generation can add current search results to responses, and search can supplement answers to out-of-domain questions.The paper also identifies query-intent analysis, engagement metrics, and unreliable-source filtering as ways search functions may address other limitations.Source: §3.4 · p. 7, §3.4.1 · p. 7, §3.4.2 · p. 7

3.5 Summary of Search4LLM Research

  • The paper’s Search4LLM vision combines intent understanding, structured answers, domain expertise, human-value alignment, relevance, and dynamic responsiveness.Its summary connects these capabilities with real-time information and cross-domain inquiries.Source: §3.5 · p. 8

4 LLM4SEARCH: AUGMENTING SEARCH ENGINES WITH LLMS

The LLM4Search section examines how large language models can augment web search. Its overview groups that augmentation around query understanding, information extraction and retrieval, and content ranking.

4 LLM4SEARCH: AUGMENTING SEARCH ENGINES WITH LLMS

  • LLMs are presented as tools for augmenting web search across query understanding, extraction, retrieval, and ranking.The overview describes these augmentations as occurring in online and offline manners.Source: §4 · p. 8
  • Query understanding is one of the areas in which LLMs can augment search.This is one of the overview’s named functions.Source: §4 · p. 8
  • Information extraction and retrieval, together with content ranking, are also named areas for LLM augmentation.Source: §4 · p. 8

4.1 Augmented Query Rewriting

The paper presents LLMs as augmenting query rewriting in search, from suggestions and corrections to extensions tailored to user context. These methods aim to improve query relevance and the search experience.

4.1.1 Query Recommendation and Completion

  • LLMs can recommend and complete queries by interpreting partial input and using query logs and behavior data to reflect current interests.The paper distinguishes predicting intended keywords from incorporating trending query patterns.Source: §4.1.1 · pp. 8–9

4.1.2 Query Correction and Improvement

  • LLMs can correct spelling and grammar in user queries so search engines can handle them and retrieve relevant results.Source: §4.1.2 · p. 9

4.1.3 Contextualized and Personalized Query Extension

  • Browsing history, prior searches, preferences, and session context can inform personalized query extensions aligned with a user’s current information-seeking task.Source: §4.1.3 · p. 9

4.2 Augmented Information Extraction and Indexing

The paper describes LLMs augmenting search-engine indexing through term extraction, summaries, semantic categorization, and generated query candidates. These functions are presented as supporting document retrieval, including for new or less-indexed content.

4.2.1 Terms Extraction and Summarization for Indexing

  • LLMs can extract precise index terms and summarize webpage content to support indexing and document retrieval.Summaries offer a quick overview without requiring users or search engines to parse the entire page.Source: §4.2 · p. 9, §4.2.1 · p. 9

4.2.2 Semantic Labeling and Categorization for Indexing

  • By measuring semantic similarity, LLMs can label and group related webpages, supporting topical retrieval and categorization.Source: §4.2.2 · pp. 9–10

4.2.3 Query Candidates Generation for Indexing

  • LLMs can generate candidate queries from webpage content as training data for neural retrievers and to help address cold starts for new or niche content.The generated queries can help search engines learn to predict and rank relevant pages when direct user-query data is limited.Source: §4.2.3 · p. 10

4.3 Augmented Information Retrieval, Document Ranking, and Content Recommendation

The section surveys LLM support for retrieval and recommendation, from relevance annotation and personalized ranking to RAG-generated conversational answers.

4.3.1 Annotation for Retrieval and Ranking

  • LLMs can provide pointwise document scores, pairwise webpage preferences, and listwise ranked lists as LTR supervision,,.Relevance annotation for query–webpage pairs is a fundamental challenge in training neural information-retrieval models,.Source: §4.3.1 · p. 10

4.3.2 Online Ranking and Recommendation for Contextual, Personalized Search

4.3.3 Retrieval-Augmented Generation (RAG) Contents for Conversational Search

  • RAG combines retrieved, ranked documents into coherent, query-relevant answers that draw on facts across sources.It uses information from the retrieved documents rather than returning only a list of documents.Source: §4.3.3 · pp. 11–12
  • RAG can condense multiple documents into accessible summaries and provide detailed, conversational answers rather than links alone.Its generated answers may include a summary, details, and references.Source: §4.3.3 · pp. 11–12

4.4 Augmented Evaluation for Search Engines

The section describes using LLMs to evaluate search variants, interpret user behavior, and turn evaluation data into experience dashboards.

4.4.1 Automated A/B Testing through User Mimicking

4.4.2 Decoding User Interactions and Intentions

  • Click sequences and query changes can help LLMs infer result relevance, shifts in user intent, and whether users find information with few interactions [50], [70], [125], [72].Source: §4.4.2 · p. 12

4.4.3 Evaluation with User Experience Dashboards

  • LLMs can aggregate interaction and comparison data, identify improvement patterns, and produce narrative dashboards for developers and designers [43],, [41], [77],.The metrics may include click-through rates, query refinements, and satisfaction indicators.Source: §4.4.3 · p. 12

4.5 Summary of LLM4Search Research

The section summarizes four LLM capabilities proposed for search: interpreting content, matching semantic relevance, modeling user context, and comparing results.

  • LLMs can extract terms and categorize web content semantically, supporting retrieval aligned with user intent.Source: §4.5 · p. 13
  • LLMs can match queries to semantically relevant content beyond keyword overlap, supporting contextual query generation and webpage ranking.Source: §4.5 · p. 13
  • By combining current queries with user history, LLMs can tailor search to individual needs and prioritize contextually relevant results.Source: §4.5 · p. 13
  • LLMs can support webpage ranking and search evaluation through relevance annotation, personalized ranking, automated A/B testing, and interaction-data analysis.Source: §4.5 · p. 13

5 CHALLENGES AND FUTURE DIRECTIONS

The paper identifies technical and societal challenges for Search4LLM and LLM4Search and outlines research directions addressing memory, explainability, agents, and responsible use.

5.1 Memory-Decomposable LLMs

  • Memory-decomposable LLMs face challenges in scaling CRUD, preserving consistency during updates, and retrieving memory accurately; proposed directions include improved architectures, incremental learning, and exact corpus recovery.Real-time updates and deletions can make model knowledge obsolete or incorrect.Source: §5.1 · p. 13

5.2 Explainability of LLMs for Web Search

  • Opaque model decisions and web-scale training complicate trust and tracing data behind misinformation or bias; proposed work includes interpretable models, XAI, and bias-aware explanations.The paper calls for balancing explainability, accuracy, and efficiency.Source: §5.2 · p. 14

5.3 Agents for Search4LLM and LLM4Search

  • Agents also need memory that adapts historical interactions to current contexts, adaptive planning, and timely, relevant actions,, [24], [103].Source: §5.3 · p. 14
  • Suggested agent research includes expanded memory spaces, dynamic planning algorithms, and learning through interactions with users, AI systems, and databases.Source: §5.3 · p. 14
  • Search agents face cross-domain transfer and real-time processing demands, alongside integration complexity across search and LLM components [103], [137].Source: §5.3 · pp. 14–15

5.4 Miscellaneous

  • The paper flags biased data, user trust, privacy, copyright, and jurisdiction-specific legal and ethical considerations in LLM-enhanced search.Source: §5.4 · p. 15
  • Proposed responses include bias detection and correction, user-facing privacy and feedback mechanisms, and cross-disciplinary work on legal and ethical AI standards.Source: §5.4 · p. 15

6 DISCUSSIONS AND CONCLUSIONS

The paper frames Search4LLM and LLM4Search as reciprocal ways for search engines and LLMs to enrich one another. It identifies integration challenges while describing the potential for more adaptive, user-centric search services.

6.1 Core Visions

  • Search4LLM uses diverse search-engine datasets and ranked documents to support LLM pre-training and fine-tuning, while LTR can refine relevance analysis and response accuracy.The focus is on improving query-context understanding and generating relevant, accurate responses.Source: §6.1 · p. 15
  • LLM4Search applies LLMs to content summarization, indexing, query optimization, document-relevance ranking, and data annotation for LTR frameworks.The paper presents these as ways LLMs could improve search-platform efficiency, accuracy, and user experience.Source: §6.1 · p. 15

6.2 Challenges and Opportunities

  • The conclusion presents both directions as opportunities to improve LLM responses and search services, including content understanding, search precision, and user satisfaction.It characterizes search-engine data as a resource for handling query complexity and LLMs as a potential source of improvements to search.Source: §6.2 · p. 15
  • The paper identifies technical hurdles, ethical concerns, training biases, and keeping datasets current with the evolving web as challenges to full integration.It presents the LLM–search-engine intersection as a basis for future research and development in services computing.Source: §6.2 · p. 15

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