title: Quantifying Generalization Complexity for Large Language Models
summary: While large language models (LLMs) have shown exceptional capabilities in
  understanding complex queries and performing sophisticated tasks, their
  generalization abilities are often deeply entangled with memorization,
  necessitating more precise evaluation. To address this challenge, we introduce
  Scylla, a dynamic evaluation framework that quantitatively measures the
  generalization abilities of LLMs. Scylla disentangles generalization from
  memorization via assessing model performance on both in-distribution (ID) and
  out-of-distribution (OOD) data through 20 tasks across 5 levels of complexity.
  Through extensive experiments, we uncover a non-monotonic relationship between
  task complexity and the performance gap between ID and OOD data, which we term
  the generalization valley. Specifically, this phenomenon reveals a critical
  threshold - referred to as critical complexity - where reliance on
  non-generalizable behavior peaks, indicating the upper bound of LLMs'
  generalization capabilities. As model size increases, the critical complexity
  shifts toward higher levels of task complexity, suggesting that larger models
  can handle more complex reasoning tasks before over-relying on memorization.
  Leveraging Scylla and the concept of critical complexity, we benchmark 28LLMs
  including both open-sourced models such as LLaMA and Qwen families, and
  close-sourced models like Claude and GPT, providing a more robust evaluation
  and establishing a clearer understanding of LLMs' generalization capabilities.
paper_authors._id: 66fe2719cbfd008a80789765
paper_authors.name: Zhenting Qi
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paper_authors.fullname: Zhenting Qi
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paper_authors.name: Hongyin Luo
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paper_authors.fullname: Hongyin Luo
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paper_authors.name: Xuliang Huang
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paper_authors.fullname: Xuliang Huang
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paper_authors.user: Xulianghuang
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paper_authors.name: Zhuokai Zhao
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paper_authors.fullname: Zhuokai Zhao
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paper_authors.user: zhuokai
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paper_authors.name: Yibo Jiang
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paper_authors.fullname: Yibo Jiang
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paper_authors.user: Nrain
paper_authors._id: 66fe2719cbfd008a8078976a
paper_authors.name: Xiangjun Fan
paper_authors.status: admin_assigned
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paper_authors.fullname: Xiangjun Fan
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paper_authors.user: maxtiktok
paper_authors._id: 66fe2719cbfd008a8078976b
paper_authors.name: Himabindu Lakkaraju
paper_authors._id: 66fe2719cbfd008a8078976c
paper_authors.name: James Glass
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-03T09:02:58.664Z
paper_authors._id: 62a793d33e101ec156cc58c4
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paper_authors.fullname: James Glass
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paper_authors.user: OmegaLittleBob
paper_title: Quantifying Generalization Complexity for Large Language Models
paper_summary: While large language models (LLMs) have shown exceptional capabilities in
  understanding complex queries and performing sophisticated tasks, their
  generalization abilities are often deeply entangled with memorization,
  necessitating more precise evaluation. To address this challenge, we introduce
  Scylla, a dynamic evaluation framework that quantitatively measures the
  generalization abilities of LLMs. Scylla disentangles generalization from
  memorization via assessing model performance on both in-distribution (ID) and
  out-of-distribution (OOD) data through 20 tasks across 5 levels of complexity.
  Through extensive experiments, we uncover a non-monotonic relationship between
  task complexity and the performance gap between ID and OOD data, which we term
  the generalization valley. Specifically, this phenomenon reveals a critical
  threshold - referred to as critical complexity - where reliance on
  non-generalizable behavior peaks, indicating the upper bound of LLMs'
  generalization capabilities. As model size increases, the critical complexity
  shifts toward higher levels of task complexity, suggesting that larger models
  can handle more complex reasoning tasks before over-relying on memorization.
  Leveraging Scylla and the concept of critical complexity, we benchmark 28LLMs
  including both open-sourced models such as LLaMA and Qwen families, and
  close-sourced models like Claude and GPT, providing a more robust evaluation
  and establishing a clearer understanding of LLMs' generalization capabilities.
paper_ai_keywords: large language models (LLMs) memorization dynamic evaluation framework in-distribution (ID) out-of-distribution (OOD) generalization valley critical complexity over-relying