Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based Perspective
Chengyin Xu, Kaiyuan Chen, Xiao Li, Ke Shen, Chenggang Li
2025-02-26
Summary
This paper talks about a new way to predict how well large AI language models will perform on different tasks before they're fully trained, using a method called Clustering-On-Difficulty (COD)
What's the problem?
As AI language models get bigger and more expensive to train, it's hard to know how well they'll do on specific tasks without training them completely. This is because their performance only becomes clear after a lot of training, and different tasks have varying levels of difficulty
What's the solution?
The researchers created COD, which groups tasks based on how difficult they are and focuses on the most predictable ones. It uses these selected tasks to estimate how the AI will do on all tasks. They also made a special way to convert the results from the selected tasks to predict performance on all tasks. They tested this method on a huge 70 billion parameter AI model and found it worked really well
Why it matters?
This matters because it can help researchers and companies save time and money when developing big AI models. By predicting performance more accurately, they can make better decisions about how to train these models and what to expect from them. This could lead to more efficient AI development and better AI systems overall
Abstract
The rapid advancements in computing dramatically increase the scale and cost of training Large Language Models (LLMs). Accurately predicting downstream task performance prior to model training is crucial for efficient resource allocation, yet remains challenging due to two primary constraints: (1) the "emergence phenomenon", wherein downstream performance metrics become meaningful only after extensive training, which limits the ability to use smaller models for prediction; (2) Uneven task difficulty distributions and the absence of consistent scaling laws, resulting in substantial metric variability. Existing performance prediction methods suffer from limited accuracy and reliability, thereby impeding the assessment of potential LLM capabilities. To address these challenges, we propose a Clustering-On-Difficulty (COD) downstream performance prediction framework. COD first constructs a predictable support subset by clustering tasks based on difficulty features, strategically excluding non-emergent and non-scalable clusters. The scores on the selected subset serve as effective intermediate predictors of downstream performance on the full evaluation set. With theoretical support, we derive a mapping function that transforms performance metrics from the predictable subset to the full evaluation set, thereby ensuring accurate extrapolation of LLM downstream performance. The proposed method has been applied to predict performance scaling for a 70B LLM, providing actionable insights for training resource allocation and assisting in monitoring the training process. Notably, COD achieves remarkable predictive accuracy on the 70B LLM by leveraging an ensemble of small models, demonstrating an absolute mean deviation of 1.36% across eight important LLM evaluation benchmarks.