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Simple argmax baseline matches LLMs in Next Activity Prediction benchmark

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Simple argmax baseline matches LLMs in Next Activity Prediction benchmark

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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]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hans Weytjens, Ingo Weber ·

    David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM

    arXiv:2606.15868v1 Announce Type: new Abstract: Next activity prediction (NAP) is a cornerstone of predictive process monitoring (PPM), enabling organizations to move from retrospective analysis to proactive process steering. The PPM field has progressed from classical machine le…

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