Papers with PROPER
PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation (2025.acl-long)
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| Challenge: | Personalized large language models (LLMs) aim to tailor outputs to user preferences . however, user data is typically sparse, making it challenging to adapt LLMs to specific user patterns. |
| Approach: | They propose a progressive learning framework that groups users based on preferences and adapts LLMs in stages. |
| Outcome: | The proposed approach outperforms SOTA models across multiple tasks. |
PROPER Agents: Proactivity Driven Personalized Agents for Advancing Knowledge Gap Navigation (2026.findings-acl)
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| Challenge: | Current approaches to proactive assistance are anchored in what users express or can read, leading to unnecessary or mistimed interventions. |
| Approach: | They propose a framework that explicitly models user-specific knowledge gaps in a controlled manner. |
| Outcome: | The proposed framework improves on quality scores and win rates across multiple domains, achieving up to 84% gains in single-turn evaluation and consistent dominance in multiturn interactions. |