ES-MemEval QA (RAG) - Information Extraction: leaderboard
Questions that need facts stated within or across sessions (271 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 find facts stated within or across sessions; 271 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-4o | 29.3 | #741 |
| 2 | Mistral Small 3.1 | 26.7 | #1699 |
| 3 | GPT-3.5 Turbo | 20.4 | #2773 |
| 4 | Phi-3-medium-128k-instruct | 13.2 | #3623 |
| 5 | Ministral-8B-Instruct-2410 | 8.4 | #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-information-extraction · How It Works · Data refreshed daily, snapshot 2026-11-12.