aiqbench analysis
This research paper evaluates the efficacy of Large Reasoning Models (LRMs) in personalization tasks, revealing that their additional reasoning capabilities do not automatically translate to superior performance compared to standard LLMs in retrieval-heavy scenarios. The authors address identified limitations like divergent thinking and format misalignment by introducing a novel framework featuring hierarchical reasoning templates and cross-referencing mechanisms to enforce consistency. Ultimately, the study provides critical insights into optimizing model architectures for specific personalization needs rather than relying solely on increased computational complexity.
Reasoning Meets Personalization: Unleashing the Potential of Large Reasoning Model for Personalized Generation
arXiv:2505.17571v2 Announce Type: replace Abstract: Personalization is a critical task in modern intelligent systems, with applications spanning diverse domains, including interactions with large language models (LLMs). Recent advances in reasoning capabilities have significantly enhanced LLMs, enabling unprecedented performance in tasks such as mathematics and coding. However, their potential for personalization tasks remains underexplored. In this paper, we present the first systematic evaluation of large reasoning models (LRMs) for personalization tasks. Surprisingly, despite generating more tokens, LRMs do not consistently outperform general-purpose LLMs, especially in retrieval-intensive scenarios where their advantages diminish. Our analysis identifies three key limitations: divergent thinking, misalignment of response formats, and ineffective use of retrieved information. To address these challenges, we propose Reinforced Reasoning for Personalization (\model), a novel framework that incorporates a hierarchical reasoning thought template to guide LRMs in generating structured outputs. Additionally, we introduce a reasoning process intervention method to enforce adherence to designed reasoning patterns, enhancing alignment. We also propose a cross-referencing mechanism to ensure consistency. Extensive experiments demonstrate that our approach significantly outperforms existing techniques.
Abstract:Personalization is a critical task in modern intelligent systems, with applications spanning diverse domains, including interactions with large language models (LLMs). Recent advances in reasoning capabilities have significantly enhanced LLMs, enabling unprecedented performance in tasks such as mathematics and coding. However, their potential for personalization tasks remains underexplored. In this paper, we present the first systematic evaluation of large reasoning models (LRMs) for personalization tasks. Surprisingly, despite generating more tokens, LRMs do not consistently outperform general-purpose LLMs, especially in retrieval-intensive scenarios where their advantages diminish. Our analysis identifies three key limitations: divergent thinking, misalignment of response formats, and ineffective use of retrieved information. To address these challenges, we propose Reinforced Reasoning for Personalization (\model), a novel framework that incorporates a hierarchical reasoning thought template to guide LRMs in generating structured outputs. Additionally, we introduce a reasoning process intervention method to enforce adherence to designed reasoning patterns, enhancing alignment. We also propose a cross-referencing mechanism to ensure consistency. Extensive experiments demonstrate that our approach significantly outperforms existing techniques.
Computation and Language (cs.CL)
arXiv:2505.17571 [cs.CL]
(or arXiv:2505.17571v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2505.17571 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 260 words
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