ES-MemEval QA (RAG) - User Modeling: leaderboard
Questions that need the user's traits, preferences and changing state inferred (251 questions). ES-MemEval (Chen et al., Tongji University, 2026) asks questions about the long emotional-support chat histories of 18 virtual users (about 22 sessions and 13K tokens each), scored by token-level F1 in percent against human-checked reference answers; higher is better. The model answers from the four most relevant full sessions, retrieved with bge-m3 from a FAISS index.
Metric: Token-level F1 (%; answers scored against human-checked reference answers; questions that infer the user's traits, preferences and changing state; 251 questions; bge-m3 retrieval of the four most relevant full sessions from a FAISS index). Source: arxiv.org. Saturation forecast: Estimated already saturated. 5 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-3.5 Turbo | 22 | #2773 |
| 2 | GPT-4o | 21.2 | #741 |
| 3 | Mistral Small 3.1 | 16.4 | #1699 |
| 4 | Phi-3-medium-128k-instruct | 13.4 | #3623 |
| 5 | Ministral-8B-Instruct-2410 | 12.6 | #4720 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=es-memeval-qa-rag-user-modeling · How It Works · Data refreshed daily, snapshot 2026-11-12.