title: Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based
    Perspective
summary: 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.
paper_authors._id: 67bd3870a917fc506d9f3d15
paper_authors.name: Chengyin Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-25T09:39:44.252Z
paper_authors._id: 66ab06956b8847339d449128
paper_authors.avatarUrl: /avatars/d71490acb91981459121005b84e556d8.svg
paper_authors.fullname: Xu Chengyin
paper_authors.type: user
paper_authors.user: JerryXu98
paper_authors._id: 67bd3870a917fc506d9f3d16
paper_authors.name: Kaiyuan Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-25T09:40:01.532Z
paper_authors._id: 636b4d796e6981ebad73f398
paper_authors.avatarUrl: /avatars/bcd405b98c12afaf1e32d85ad8ce7f23.svg
paper_authors.fullname: Kaiyuan Chen
paper_authors.type: user
paper_authors.user: Lucky2022
paper_authors._id: 67bd3870a917fc506d9f3d17
paper_authors.name: Xiao Li
paper_authors._id: 67bd3870a917fc506d9f3d18
paper_authors.name: Ke Shen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-25T09:39:49.578Z
paper_authors._id: 645604eebabbbbd3486dc615
paper_authors.avatarUrl: /avatars/17a5ca8274e2bfc8f183a4af9878a930.svg
paper_authors.fullname: shenke
paper_authors.type: user
paper_authors.user: shenke18
paper_authors._id: 67bd3870a917fc506d9f3d19
paper_authors.name: Chenggang Li
paper_title: Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based
    Perspective
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) emergence phenomenon downstream task performance resource allocation Clustering-On-Difficulty (COD) difficulty features intermediate predictors performance metrics mapping function performance scaling ensemble of small models absolute mean deviation LLM evaluation benchmarks