Quantifying Generalization Complexity for Large Language Models
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.
66fe2719cbfd008a80789765
Zhenting Qi
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2024-10-03T15:01:56.628Z
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Zhenting Qi
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zhenting
66fe2719cbfd008a80789766
Hongyin Luo
admin_assigned
2024-10-03T09:02:01.424Z
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Hongyin Luo
user
luohy
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Xuliang Huang
admin_assigned
2024-10-03T09:02:22.898Z
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Xuliang Huang
user
Xulianghuang
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Zhuokai Zhao
admin_assigned
2024-10-03T09:02:30.772Z
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Zhuokai Zhao
user
zhuokai
66fe2719cbfd008a80789769
Yibo Jiang
admin_assigned
2024-10-03T09:02:37.313Z
65e53fdf00680b19bc6fc834
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Yibo Jiang
user
Nrain
66fe2719cbfd008a8078976a
Xiangjun Fan
admin_assigned
2024-10-03T09:02:45.309Z
64c2e1f785f7034e2eefd8dc
/avatars/5f0affc5dfde833431494621d5fb3037.svg
Xiangjun Fan
user
maxtiktok
66fe2719cbfd008a8078976b
Himabindu Lakkaraju
66fe2719cbfd008a8078976c
James Glass
admin_assigned
2024-10-03T09:02:58.664Z
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/avatars/d3607cdb2cac1b2a3011d7441e6ea321.svg
James Glass
user
OmegaLittleBob
Quantifying Generalization Complexity for Large Language Models
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.
Scylla, a dynamic evaluation framework, measures LLM generalization by assessing performance on in-distribution and out-of-distribution data, revealing a non-monotonic relationship between task complexity and generalization.
Scylla generalization abilities memorization in-distribution data out-of-distribution data generalization valley critical complexity LLaMA Qwen Claude GPT