# Chit-Chat or Deep Talk: Prompt Engineering for Process Mining

arXiv: https://arxiv.org/abs/2307.09909 · HTML: https://mlsanity.com/paper/2307.09909/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA](https://mlsanity.com/paper/2109.05014/) (2021)
- [AI-Augmented Business Process Management Systems: A Research Manifesto](https://mlsanity.com/paper/2201.12855/) (2022)

## Built on by (most cited first, 9 of 9)

- [PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks](https://mlsanity.com/paper/2407.13244/) (2024)
- [Evaluating the Ability of LLMs to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2407.02310/) (2024)
- [Re-Thinking Process Mining in the AI-Based Agents Era](https://mlsanity.com/paper/2408.07720/) (2024)
- [LLM-based event abstraction and integration for IoT-sourced logs](https://mlsanity.com/paper/2409.03478/) (2024)
- [Automated Business Process Analysis: An LLM-Based Approach to Value Assessment](https://mlsanity.com/paper/2504.06600/) (2025)
- [On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks](https://mlsanity.com/paper/2504.21074/) (2025)
- [Conversational Process Model Redesign](https://mlsanity.com/paper/2505.05453/) (2025)
- [Evaluating LLM-Based Process Explanations under Progressive Behavioral-Input Reduction](https://mlsanity.com/paper/2510.09732/) (2025)
- [LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines](https://mlsanity.com/paper/2606.09489/) (2026)

## Abstract (arXiv)

This research investigates the application of Large Language Models (LLMs) to augment conversational agents in process mining, aiming to tackle its inherent complexity and diverse skill requirements. While LLM advancements present novel opportunities for conversational process mining, generating efficient outputs is still a hurdle. We propose an innovative approach that amend many issues in existing solutions, informed by prior research on Natural Language Processing (NLP) for conversational agents. Leveraging LLMs, our framework improves both accessibility and agent performance, as demonstrated by experiments on public question and data sets. Our research sets the stage for future explorations into LLMs' role in process mining and concludes with propositions for enhancing LLM memory, implementing real-time user testing, and examining diverse data sets.