PersEvalBench: Can LLM Evaluators Judge Contextually Appropriate Use of Persona Information?
cs.CL
Submitted: 2026-10-13
Updated: 2026-10-13
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
- How Does Personalized Memory Shape LLM Behavior? Benchmarking Rational Preference Utilization in Personalized Assistants
- GLM-5: from Vibe Coding to Agentic Engineering
- Towards Natural Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions
- OP-Bench: Benchmarking Over-Personalization for Memory-Augmented Personalized Conversational Agents
- Kimi K2.5: Visual Agentic Intelligence
- HorizonBench: Long-Horizon Personalization with Evolving Preferences
- CIMemories: A Compositional Benchmark for Contextual Integrity of Persistent Memory in LLMs
- Qwen3 Technical Report
- BenchPreS: A Benchmark for Context-Aware Personalized Preference Selectivity of Persistent-Memory LLMs
Related papers
- Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
- Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
- Subliminal Steering: Stronger Encoding of Hidden Signals
- MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
- The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
- Untangling the Mechanisms of Misleading Context in Medical Question Answering