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STEM: Efficient Relative Capability Evaluation of LLMs through Structured Transition Samples

Created by
  • Haebom

Author

Haiquan Hu, Jiazhi Jiang, Shiyou Xu, Ruhan Zeng, Tian Wang

Outline

To address the growing challenge of evaluating large-scale language models (LLMs), this paper proposes a novel evaluation framework, the Structured Transition Evaluation Method (STEM). STEM analyzes the performance variations of LLMs with identical architectures but different parameter sizes to identify significant transition samples (STS). These STS are then used to efficiently and interpretably estimate the performance of unknown models. Using the Qwen3 model, we build a pool of STSs across six diverse benchmarks. Experimental results demonstrate that STEM reliably captures model performance trends and matches ground-truth performance rankings. This highlights STEM as a practical and scalable method for fine-tuning and architecture-independent evaluation of LLMs.

Takeaways, Limitations

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Takeaways:
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Presenting a novel method that can significantly improve the efficiency and interpretability of LLM assessment.
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Effectively solves the overfitting and high computational cost problems of existing benchmarks.
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Enables architecture-independent, fine-tuned LLM performance comparisons.
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Provides reliable evaluation results that closely match actual performance rankings.
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Limitations:
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Dependency on the Qwen3 model used to build the STS pool. Further verification of generalization performance on LLMs with other architectures is needed.
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Further research is needed on the objectivity and generalizability of STS selection criteria.
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Further extensive experiments and validation of various types of LLMs are needed.
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