Log_b Quant is an adjustable-base logarithmic quantization technique that outperforms tensor-wise asymmetric linear quantization at 4-bit precision on language model benchmarks while providing memory savings.
ADALog: Adaptive unsupervised anomaly detection in logs with self-attention masked language model
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Systematic review of 145 papers on LLM-based log analysis, providing a unified taxonomy, common design patterns, evaluation practices, and challenges for deployment under drift and limited labels.
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$\text{Log}_\text{b}$Quant: Quantizing Language Models in Logarithmic Space
Log_b Quant is an adjustable-base logarithmic quantization technique that outperforms tensor-wise asymmetric linear quantization at 4-bit precision on language model benchmarks while providing memory savings.
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LLM4Log: A Systematic Review of Large Language Model-based Log Analysis
Systematic review of 145 papers on LLM-based log analysis, providing a unified taxonomy, common design patterns, evaluation practices, and challenges for deployment under drift and limited labels.