arXiv:2606.26429v1 Announce Type: new Abstract: Current LLM evaluation relies on two complementary but often disconnected signals: static benchmarks with objective correctness labels and arena-style preference data that better reflect open-ended user interactions. We introduce DualEval, a latent model-item calibration framework that represents models and evaluation items in a shared space, jointly estimating model ability together with item difficulty and sharpness. We apply DualEval across four domains: coding, math, miscellaneous domain-knowledge tasks, and generic everyday user queries. Our
The proliferation of LLMs and the increasing complexity of their applications necessitates more robust and unified evaluation methodologies beyond simplistic benchmarks or subjective preference data.
A unified evaluation framework like DualEval offers a more accurate, holistic understanding of LLM capabilities and limitations, which is crucial for advancing AI research, development, and deployment.
LLM evaluation may become less fragmented and more standardized, allowing for more reliable comparisons between models and better identification of areas for improvement.
- · AI researchers
- · LLM developers
- · Enterprises deploying LLMs
- · AI evaluation platforms
- · Ad-hoc LLM evaluation methods
- · Developers relying solely on preference data
Improved model selection and fine-tuning for specific applications will result from more precise evaluation.
Faster innovation cycles in LLM development due to clearer performance signals and better identification of weaknesses.
Enhanced trust and adoption of LLM technologies across critical sectors as evaluation becomes more robust and transparent.
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Read at arXiv cs.LG