Furyton/awesome-language-model-analysis
This paper list focuses on the theoretical and empirical analysis of language models, especially large language models (LLMs). The papers in this list investigate the learning behavior, generalization ability, and other properties of language models through theoretical analysis, empirical analysis, or a combination of both.
This list compiles research papers that delve into the fundamental behaviors and properties of large language models (LLMs), especially those based on transformer architecture. It serves as a curated resource for understanding how these models learn, generalize, and exhibit various phenomena. Researchers, academics, and advanced students in AI and machine learning fields would find this valuable for literature reviews and deeper theoretical study.
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Use this if you are an AI researcher or academic seeking to understand the underlying mechanisms, strengths, and limitations of transformer-based large language models rather than looking for ways to improve their performance.
Not ideal if you are looking for practical guides, code implementations, or papers focused on improving the performance or fine-tuning of language models for specific applications.
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Dec 02, 2024
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