# BESPOKE: Benchmark for Search-Augmented Large Language Model Personalization via Diagnostic Feedback

arXiv: https://arxiv.org/abs/2509.21106 · HTML: https://mlsanity.com/paper/2509.21106/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2025.

## Built on by (most cited first, 7 of 7)

- [AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping](https://mlsanity.com/paper/2602.12315/) (2026)
- [P-Check: Advancing Personalized Reward Model via Learning to Generate Dynamic Checklist](https://mlsanity.com/paper/2601.02986/) (2026)
- [SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization](https://mlsanity.com/paper/2602.12187/) (2026)
- [A Survey of Reasoning-Intensive Retrieval: Progress and Challenges](https://mlsanity.com/paper/2605.00063/) (2026)
- [Personalized Deep Research Query Refinement with Graph-Scaffolded Evidence Grounding](https://mlsanity.com/paper/2608.05876/) (2026)
- [SocialPersona: Benchmarking Personalized Profiling and Response with Multimodal Social-Media Context](https://mlsanity.com/paper/2606.26654/) (2026)
- [ExpVoyager: Direct Experience Navigation for Dynamic Agent Skill Synthesis](https://mlsanity.com/paper/2609.32630/) (2026)

## Abstract (arXiv)

Search-augmented large language models (LLMs) have advanced information-seeking tasks by integrating retrieval into generation, reducing users' cognitive burden compared to traditional search systems. Yet they remain insufficient for fully addressing diverse user needs, which requires recognizing how the same query can reflect different intents across users and delivering information in preferred forms. While recent systems such as ChatGPT and Gemini attempt personalization by leveraging user histories, systematic evaluation of such personalization is under-explored. To address this gap, we propose BESPOKE, the realistic benchmark for evaluating personalization in search-augmented LLMs. BESPOKE is designed to be both realistic, by collecting authentic chat and search histories directly from humans, and diagnostic, by pairing responses with fine-grained preference scores and feedback. The benchmark is constructed through long-term, deeply engaged human annotation, where human annotators contributed their own histories, authored queries with detailed information needs, and evaluated responses with scores and diagnostic feedback. Leveraging BESPOKE, we conduct systematic analyses that reveal key requirements for effective personalization in information-seeking tasks, providing a foundation for fine-grained evaluation of personalized search-augmented LLMs. Our code and data are available at https://augustinlib.github.io/BESPOKE/.