# CUPID: Evaluating Personalized and Contextualized Alignment of LLMs from Interactions

arXiv: https://arxiv.org/abs/2508.01674 · HTML: https://mlsanity.com/paper/2508.01674/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 62 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 30 of 44)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [OpenAssistant Conversations -- Democratizing Large Language Model Alignment](https://mlsanity.com/paper/2304.07327/) (2023)
- [LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory](https://mlsanity.com/paper/2410.10813/) (2024)
- [MemoryBank: Enhancing Large Language Models with Long-Term Memory](https://mlsanity.com/paper/2305.10250/) (2023)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://mlsanity.com/paper/2406.20094/) (2024)
- [SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents](https://mlsanity.com/paper/2310.11667/) (2023)
- [GPTScore: Evaluate as You Desire](https://mlsanity.com/paper/2302.04166/) (2023)
- [Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging](https://mlsanity.com/paper/2310.11564/) (2023)
- [AI Alignment: A Comprehensive Survey](https://mlsanity.com/paper/2310.19852/) (2023)
- [WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild](https://mlsanity.com/paper/2406.04770/) (2024)
- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets](https://mlsanity.com/paper/2307.10928/) (2023)
- [Augmenting Language Models with Long-Term Memory](https://mlsanity.com/paper/2306.07174/) (2023)
- [A Roadmap to Pluralistic Alignment](https://mlsanity.com/paper/2402.05070/) (2024)
- [Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs](https://mlsanity.com/paper/2502.09597/) (2025)
- [The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models](https://mlsanity.com/paper/2404.16019/) (2024)
- [Training Language Models with Memory Augmentation](https://mlsanity.com/paper/2205.12674/) (2022)
- [STaR-GATE: Teaching Language Models to Ask Clarifying Questions](https://mlsanity.com/paper/2403.19154/) (2024)
- [Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation](https://mlsanity.com/paper/2404.05970/) (2024)

## Built on by (most cited first, 22 of 22)

- [LifeSim: Long-Horizon User Life Simulator for Personalized Assistant Evaluation](https://mlsanity.com/paper/2603.12152/) (2026)
- [DiscoverLLM: From Executing Intents to Discovering Them](https://mlsanity.com/paper/2602.03429/) (2026)
- [From Words to Widgets for Controllable LLM Generation](https://mlsanity.com/paper/2604.10925/) (2026)
- [Synthetic Interaction Data for Scalable Personalization in Large Language Models](https://mlsanity.com/paper/2602.12394/) (2026)
- [Contextualized Visual Personalization in Vision-Language Models](https://mlsanity.com/paper/2602.03454/) (2026)
- [Language Models Don't Know What You Want: Evaluating Personalization in Deep Research Needs Real Users](https://mlsanity.com/paper/2603.16120/) (2026)
- [MedConceal: A Benchmark for Clinical Hidden-Concern Reasoning Under Partial Observability](https://mlsanity.com/paper/2604.08788/) (2026)
- [Latent Preference Modeling for Multi-Session Personalized Tool Calling](https://mlsanity.com/paper/2604.17886/) (2026)
- [PersonaTree: Structured Lifecycle Memory for Person Understanding in LLM Agents](https://mlsanity.com/paper/2606.04780/) (2026)
- [LikeBench: Evaluating Subjective Likability in LLMs for Personalization](https://mlsanity.com/paper/2512.13077/) (2025)
- [Omni-Persona: Systematic Benchmarking and Improving Omnimodal Personalization](https://mlsanity.com/paper/2605.09996/) (2026)
- [Re-Centering Humans in LLM Personalization](https://mlsanity.com/paper/2606.06614/) (2026)
- [Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data](https://mlsanity.com/paper/2603.19294/) (2026)
- [Mitigating LLM biases toward spurious social contexts using direct preference optimization](https://mlsanity.com/paper/2604.02585/) (2026)
- [Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences](https://mlsanity.com/paper/2606.07629/) (2026)
- [Identifying Harm in Personalized, Generative AI Systems Requires User-Centered Auditing at the Interaction Level](https://mlsanity.com/paper/2608.14692/) (2026)
- [Evaluating the Hidden Costs of Personalization in Large Language Models](https://mlsanity.com/paper/2608.28833/) (2026)
- [Over-Personalization Is a Decision Failure: Generation-Induced Apply Bias in LLMs](https://mlsanity.com/paper/2609.34284/) (2026)
- [PairPref: When Should Memory Guide the Answer? A Benchmark for Contextual Preference Use](https://mlsanity.com/paper/2609.34526/) (2026)
- [AdaptArena: Evaluating Test-Time Personalization of Web Agents](https://mlsanity.com/paper/2609.36488/) (2026)
- [RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations](https://mlsanity.com/paper/2610.01780/) (2026)
- [Knowing the User Is Not Knowing What We Established: Grounding Awareness in Personal Agents](https://mlsanity.com/paper/2610.18684/) (2026)

## Abstract (arXiv)

Personalization of Large Language Models (LLMs) often assumes users hold static preferences that reflect globally in all tasks. In reality, humans hold dynamic preferences that change depending on the context. As users interact with an LLM in various contexts, they naturally reveal their contextual preferences, which a model must infer and apply in future contexts to ensure alignment. To assess this, we introduce CUPID, a benchmark of 756 human-curated interaction session histories between users and LLM-based chat assistants. In each interaction session, the user provides a request in a specific context and expresses their preference through multi-turn feedback. Given a new user request and prior interaction sessions, our benchmark assesses whether LLMs can infer the preference relevant to this request and generate a response that satisfies this preference. With CUPID, we evaluated 10 open and proprietary LLMs, revealing that state-of-the-art LLMs struggle to infer preferences from multi-turn interactions and fail to discern what previous context is relevant to a new request -- under 50% precision and 65% recall. Our work highlights the need to advance LLM capabilities for more contextually personalized interactions and proposes CUPID as a resource to drive these improvements.