Survey: AI Search with Large Language Models (July 2025 Preprint)
- Source: https://www.preprints.org/frontend/manuscript/79453d62cbbfce9ac42239071098a3d9/download_pub
- Type: Survey (preprint, July 2025, not peer reviewed)
- Section: Search Surveys
- Dense retrieval: high recall, low latency; suited for first-stage retrieval
- Late interaction (e.g., ColBERT): higher accuracy but larger indexes
- Generative IR: directly "generates" documents via tokens or docids, simplifying cascades
- Agentic search: models search as sequential decision-making, supporting multi-hop reasoning and self-reflection
- 2019–2021: BERT reranking and DPR establish neural retrieval foundations
- 2022–2023: RAG and FreshLLM drive retrieval-generation fusion
- 2024 onward: conversational/agentic search and Gen-RecSys surge
- 2025–2026: RL-trained search agents, Deep Research, and GraphRAG emerge as growth areas
- Datasets: MS MARCO, BEIR, Natural Questions, domain corpora
- Metrics: nDCG@10, MRR, Recall@k, Hit@k, human preference, task success rate, latency, token cost
- Baselines: BM25, dense retrieval, cross-encoder reranking, no-retrieval LLMs, commercial search APIs
- Exact quantitative results should be verified against the original PDF.
- A Comprehensive Survey on Reinforcement Learning-based Agentic Search (arXiv:2510.16724)
- A Survey of Conversational Search (arXiv:2410.15576)
- A Survey of Large Language Model Empowered Agents for Recommendation (arXiv:2503.05659)
- A Survey of Model Architectures in Information Retrieval (arXiv:2502.14822)
- A Survey on Knowledge-Oriented Retrieval-Augmented Generation (arXiv:2503.10677)
- Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
Key points
This survey reviews the intersection of large language models (LLMs) and AI search, covering open-domain information access, enterprise knowledge retrieval, conversational search, semantic understanding in recommender systems, and end-to-end architectures that combine external knowledge sources with generative models.
Taxonomy
| Dimension | Subcategories | Representative ideas | Strengths | Limitations | |---|---|---|---|---| | Modeling paradigm | Discriminative / generative retrieval | Bi-encoders, cross-encoders, DSI, GPT indexing | Mature, scalable | Semantic drift, update cost | | LLM integration | RAG / Agent / Tool-use | Retrieval augmentation, search agents, API calls | Flexible, interpretable | Latency, error propagation | | Optimization objectives | Relevance / diversity / freshness | Multi-objective LTR, RLHF, online learning | Business-aligned | Scarce annotations | | Evaluation | Offline / Online / Human | nDCG, MRR, LLM-as-judge, A/B tests | Comparable | Diverges from real satisfaction |
Four main research lines
Timeline of evolution
Evaluation paradigm
Insights for search / recommendation / personalization
1. Architecture: cascade retrieval + rerank + generation remains mainstream, but the agentic paradigm makes retrieval count and strategy learnable 2. Data: high-quality instruction data and click/session logs are critical; synthetic data needs leakage and distribution-shift safeguards 3. Evaluation: offline metrics diverge from online satisfaction; LLM-as-judge should be cross-validated with human evaluation 4. Product: latency, cost, explainability, and safety are hard deployment constraints—do not optimize academic benchmarks alone
Open problems
Lack of unified benchmarks, non-reproducible private data, LLM evaluation bias, and safety/cost constraints of agentic systems. Future directions include finer-grained process supervision, joint retrieval-reasoning training, enterprise metadata governance, and multimodal/cross-lingual consistency.