# Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications

arXiv: https://arxiv.org/abs/2311.12287 · HTML: https://mlsanity.com/paper/2311.12287/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [ProcessGPT: Transforming Business Process Management with Generative Artificial Intelligence](https://mlsanity.com/paper/2306.01771/) (2023)
- [Towards Personalized and Human-in-the-Loop Document Summarization](https://mlsanity.com/paper/2108.09443/) (2021)
- [Adaptive Summaries: A Personalized Concept-based Summarization Approach by Learning from Users' Feedback](https://mlsanity.com/paper/2012.13387/) (2020)
- [Are We On The Same Page? Hierarchical Explanation Generation for Planning Tasks in Human-Robot Teaming using Reinforcement Learning](https://mlsanity.com/paper/2012.11792/) (2020)
- [Am I Rare? An Intelligent Summarization Approach for Identifying Hidden Anomalies](https://mlsanity.com/paper/2012.15755/) (2020)
- [A Query Language for Summarizing and Analyzing Business Process Data](https://mlsanity.com/paper/2105.10911/) (2021)
- [A Personalized Reinforcement Learning Summarization Service for Learning Structure from Unstructured Data](https://mlsanity.com/paper/2307.05696/) (2023)

## Built on by (most cited first, 8 of 8)

- [When Search Engine Services meet Large Language Models: Visions and Challenges](https://mlsanity.com/paper/2407.00128/) (2024)
- [Stereotype or Personalization? User Identity Biases Chatbot Recommendations](https://mlsanity.com/paper/2410.05613/) (2024)
- [MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation](https://mlsanity.com/paper/2407.01972/) (2024)
- [TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval](https://mlsanity.com/paper/2502.20969/) (2025)
- [EnronQA: Towards Personalized RAG over Private Documents](https://mlsanity.com/paper/2505.00263/) (2025)
- [Explanation, Debate, Align: A Weak-to-Strong Framework for Language Model Generalization](https://mlsanity.com/paper/2409.07335/) (2024)
- [Optimizing Web-Based AI Query Retrieval with GPT Integration in LangChain A CoT-Enhanced Prompt Engineering Approach](https://mlsanity.com/paper/2506.15512/) (2025)
- [WaterMoE: Expert-Routing-based Watermarking for High Fidelity and Efficiency](https://mlsanity.com/paper/2607.13099/) (2026)

## Abstract (arXiv)

The advent of Large Language Models (LLMs) heralds a pivotal shift in online user interactions with information. Traditional Information Retrieval (IR) systems primarily relied on query-document matching, whereas LLMs excel in comprehending and generating human-like text, thereby enriching the IR experience significantly. While LLMs are often associated with chatbot functionalities, this paper extends the discussion to their explicit application in information retrieval. We explore methodologies to optimize the retrieval process, select optimal models, and effectively scale and orchestrate LLMs, aiming for cost-efficiency and enhanced result accuracy. A notable challenge, model hallucination-where the model yields inaccurate or misinterpreted data-is addressed alongside other model-specific hurdles. Our discourse extends to crucial considerations including user privacy, data optimization, and the necessity for system clarity and interpretability. Through a comprehensive examination, we unveil not only innovative strategies for integrating Language Models (LLMs) with Information Retrieval (IR) systems, but also the consequential considerations that underline the need for a balanced approach aligned with user-centric principles.