arXiv:2606.03189v1 Announce Type: new Abstract: Large Language Models (LLMs) as judges across various scenarios such as assessing model responses is becoming an increasingly accepted paradigm. However, existing judgment approaches often rely on trained judgers using fixed preference data, which tend to overlook diverse user preferences and struggle to adapt to real-world human-AI dialogue scenarios. To address these limitations, we propose SenseJudge, a customizable judgment framework driven by human preferences and SenseBench, a diverse and challenging instruction-following benchmark derived
As LLMs become ubiquitous in various judgmental roles, the limitations of fixed preference data and the need for human-centric adaptability are becoming critical challenges.
This development addresses how LLMs assess and interact, moving towards more nuanced and user-aligned outcomes crucial for broad AI adoption and impactful applications.
Judgment frameworks for LLMs will become more customizable and reflective of diverse human preferences, moving beyond rigid, pre-trained paradigms.
- · AI developers
- · End-users of AI
- · AI ethics researchers
- · Companies seeking adaptable AI solutions
- · Developers relying solely on fixed, monolithic AI judgment models
- · Standardized AI evaluation metrics that lack customizability
LLMs will be able to perform judgmental tasks with greater accuracy and relevance to individual user needs and contexts.
This could lead to a proliferation of highly bespoke AI assistant applications tailored to specific user preferences and values.
The increased fidelity of AI judgment might accelerate the integration of AI into complex decision-making processes across industries, potentially impacting professional services.
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Read at arXiv cs.CL