# Exploring LLM Features in Predictive Process Monitoring for Small-Scale Event-Logs

arXiv: https://arxiv.org/abs/2601.11468 · HTML: https://mlsanity.com/paper/2601.11468/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 15 of 15)

- [Large Language Models Are Zero-Shot Time Series Forecasters](https://mlsanity.com/paper/2310.07820/) (2023)
- [Long-context LLMs Struggle with Long In-context Learning](https://mlsanity.com/paper/2404.02060/) (2024)
- [BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack](https://mlsanity.com/paper/2406.10149/) (2024)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language](https://mlsanity.com/paper/2405.12856/) (2024)
- [Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring](https://mlsanity.com/paper/1805.02896/) (2018)
- [Explainable Predictive Process Monitoring](https://mlsanity.com/paper/2008.01807/) (2020)
- [Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models](https://mlsanity.com/paper/2503.22165/) (2025)
- [Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring](https://mlsanity.com/paper/2107.01905/) (2021)
- [PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks](https://mlsanity.com/paper/2407.13244/) (2024)
- [PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances](https://mlsanity.com/paper/2404.06267/) (2024)
- [Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2407.02310/) (2024)
- [An Explainable Decision Support System for Predictive Process Analytics](https://mlsanity.com/paper/2207.12782/) (2022)
- [Trace Encoding in Process Mining: a survey and benchmarking](https://mlsanity.com/paper/2301.02167/) (2023)
- [Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring](https://mlsanity.com/paper/2403.11642/) (2024)

## Abstract (arXiv)

Predictive Process Monitoring is a branch of process mining that aims to predict the outcome of an ongoing process. Recently, it leveraged machine-and-deep learning architectures. In this paper, we extend our prior LLM-based Predictive Process Monitoring framework, which was initially focused on total time prediction via prompting. The extension consists of comprehensively evaluating its generality, semantic leverage, and reasoning mechanisms, also across multiple Key Performance Indicators. Empirical evaluations conducted on three distinct event logs and across the Key Performance Indicators of Total Time and Activity Occurrence prediction indicate that, in data-scarce settings with only 100 traces, the LLM surpasses the benchmark methods. Furthermore, the experiments also show that the LLM exploits both its embodied prior knowledge and the internal correlations among training traces. Finally, we examine the reasoning strategies employed by the model, demonstrating that the LLM does not merely replicate existing predictive methods but performs higher-order reasoning to generate the predictions.