Exploring the Potential of LLMs as Personalized Assistants: Dataset, Evaluation, and Analysis (2506.01262v1)

Abstract: Personalized AI assistants, a haLLMark of the human-like capabilities of LLMs, are a challenging application that intertwines multiple problems in LLM research. Despite the growing interest in the development of personalized assistants, the lack of an open-source conversational dataset tailored for personalization remains a significant obstacle for researchers in the field. To address this research gap, we introduce HiCUPID, a new benchmark to probe and unleash the potential of LLMs to deliver personalized responses. Alongside a conversational dataset, HiCUPID provides a Llama-3.2-based automated evaluation model whose assessment closely mirrors human preferences. We release our dataset, evaluation model, and code at https://github.com/12kimih/HiCUPID.

Exploring the Potential of LLMs as Personalized Assistants

The paper "Exploring the Potential of LLMs as Personalized Assistants: Dataset, Evaluation, and Analysis" presents HiCUPID, a comprehensive benchmark designed to evaluate and enhance the personalization capabilities of LLMs. The primary aim is to address the gap in available resources for training and evaluating personalized AI assistants—an area increasingly pertinent as LLMs become more integrated into human activities.

Contributions of HiCUPID

1. Benchmark Configuration: HiCUPID serves as a novel benchmark specifically crafted to test LLMs' ability to generate personalized responses based on detailed user profiles and interaction histories. Each synthetic user is characterized by rich metadata, including 25 persona dimensions, a profile, and schedules. The dataset is structured to reflect five key aspects of personalized assistants: adherence to user information, understanding implicit information, reasoning from multiple contexts, long-context handling, and proactive responses.
2. Comparison Against Existing Datasets: HiCUPID is contrasted with existing personalization datasets, highlighting its expansive scope and alignment with real-world complexities of personalized assistant tasks. Where traditional datasets focus on text classification, HiCUPID encompasses the generation challenge with detailed, multi-turn dialogues.
3. Evaluation Methodology: The paper employs a two-tiered evaluation approach:
   • Human Preference Estimation: Leveraging human-like judgment via GPT-4o evaluation, HiCUPID aligns with known human preferences, thereby providing reliable assessment criteria for LLMs.
   • Automated Evaluation Models: A Llama-3.2-based proxy evaluator is trained to emulate human preference assessments, mitigating costs associated with large-scale human evaluations.

Strong Numerical Results and Insights

• The experiments demonstrate that current state-of-the-art LLMs, including both closed-source (GPT-4o-mini) and open-source models (Llama-3.1-8B, Mistral-7B, Qwen-2.5-7B), exhibit varied success in personalization tasks as characterized by HiCUPID.
• Supervised Fine-tuning (SFT) emerges as the most effective method across models, significantly improving LLMs' responsiveness to personalized queries.
• Direct Preference Optimization (DPO) and combinations of SFT followed by DPO show promise but are less consistent, particularly when addressing the multi-info reasoning challenges set by HiCUPID.

Practical and Theoretical Implications

The findings suggest avenues for practical enhancements in AI personalization technologies, particularly emphasizing the importance of model fine-tuning strategies that better capture user nuances and preferences. On a theoretical level, HiCUPID announces the demand for increased focus on integrating multi-contextual reasoning and improved long-context modeling within LLMs—areas where existing models underperform according to the benchmark criteria.

Prospects for Future AI Developments

HiCUPID sets the groundwork for creating more adept and personalized AI systems, laying out the challenges that future research must address. This involves developing more sophisticated retrieval and reasoning algorithms to handle extensive user interaction histories and fostering LLMs' ability to synthesize data from various sources into coherent and user-tailored outputs.

In summary, this paper establishes HiCUPID as a robust and critical resource for advancing LLM-powered personalized assistants, providing a significant stride forward amidst the barriers faced in current personalization endeavors. While the current results underscore both the potential and limitations of existing LLM architectures, the research brings clarity to the directives necessary for developing truly personalized machine intelligence.