Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based Perspective

• URL: http://arxiv.org/abs/2502.17262v2
• Date: Fri, 23 May 2025 09:30:02 GMT
• Title: Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based Perspective
• Authors: Chengyin Xu, Kaiyuan Chen, Xiao Li, Ke Shen, Chenggang Li,
• Abstract summary: The escalating scale and cost of Large Language Models (LLMs) training necessitate accurate pre-training prediction of downstream task performance.<n>Current prediction methods lack accuracy and reliability.<n>We propose a Clustering-On-Difficulty (COD) framework for downstream performance prediction.
• Score: 5.09611816929943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The escalating scale and cost of Large Language Models (LLMs) training necessitate accurate pre-training prediction of downstream task performance for efficient resource allocation. This is challenged by: 1) the emergence phenomenon, where metrics become meaningful only after extensive training, hindering prediction by smaller models; and 2) uneven task difficulty and inconsistent performance scaling patterns, leading to high metric variability. Current prediction methods lack accuracy and reliability. We propose a Clustering-On-Difficulty (COD) framework for downstream performance prediction. The COD framework clusters tasks by their difficulty scaling features, thereby establishing a more stable and predictable support subset through the exclusion of tasks exhibiting non-emergent behavior or irregular scaling. We adopt a performance scaling law to predict cluster-wise performance with theoretical support. Predictable subset performance acts as an intermediate predictor for the full evaluation set. We further derive a mapping function to accurately extrapolate the performance of the subset to the full set. Applied to an LLM with 70B parameters, COD achieved a 1.36% average prediction error across eight key LLM benchmarks, offering actionable insights for resource allocation and training monitoring of LLMs pretraining.

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