Simple argmax baseline matches LLMs in Next Activity Prediction benchmark

A new paper on arXiv benchmarks the performance of various models for Next Activity Prediction (NAP), a key component of predictive process monitoring. The study compares large language models (LLMs), Transformers, LSTMs, and a simple argmax baseline across seven real-life event logs. Surprisingly, the results indicate that pretraining and model size do not consistently improve performance, and the basic argmax baseline often matches or rivals that of billion-parameter LLMs. AI

RANK_REASON The cluster contains an academic paper published on arXiv detailing a benchmark comparison of AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• large language models
• long short-term memory
• Next Activity Prediction
• Predictive Process Monitoring
• transformer

• paper
• other

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