awesome-language-model-analysis

github.com/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.

103

GitHub Stars

738

Curated Resources

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What's inside

Miscellanea

Phenomena of Interest

[paper link]In-Context Learning
[paper link]In-Context Learning
[paper link]In-Context Learning
[paper link]In-Context Learning
[paper link]In-Context Learning
[paper link]Training Dynamics / Landscape / Optimization / Fine-tuning / etc.

Representational Capacity

[paper link]What Can Transformer Do? / Properties of Transformer
[paper link]What Can Transformer Do? / Properties of Transformer
[paper link]What Can Transformer Do? / Properties of Transformer
[paper link]What Can Transformer Not Do? / Limitation of Transformer
[paper link]What Can Transformer Not Do? / Limitation of Transformer
[paper link]What Can Transformer Not Do? / Limitation of Transformer

Mechanistic Engineering / Probing / Interpretability

Training Paradigms

Architectural Effectivity

[paper link]Linear Attention / State Space Models / Recurrent Language Models / etc.
[paper link]Tokenization / Embedding
[paper link]Layer-normalization
[paper link]Linear Attention / State Space Models / Recurrent Language Models / etc.

Showing a sample of 738 resources. View the full list on GitHub →