Chit-Chat or Deep Talk: Prompt Engineering for Process Mining
- URL: http://arxiv.org/abs/2307.09909v1
- Date: Wed, 19 Jul 2023 11:25:12 GMT
- Title: Chit-Chat or Deep Talk: Prompt Engineering for Process Mining
- Authors: Urszula Jessen, Michal Sroka, Dirk Fahland
- Abstract summary: This research investigates the application of Large Language Models (LLMs) to augment conversational agents in process mining.
We propose an innovative approach that amend many issues in existing solutions, informed by prior research on Natural Language Processing (NLP) for conversational agents.
Our framework improves both accessibility and agent performance, as demonstrated by experiments on public question and data sets.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks [0.0]
Large language models (LLMs) have shown remarkable performance on many different Natural Language Processing (NLP) tasks.
Prompt engineering plays a key role in adding more to the already existing abilities of LLMs to achieve significant performance gains.
This paper summarizes different prompting techniques and club them together based on different NLP tasks that they have been used for.
arXiv Detail & Related papers (2024-07-17T20:23:19Z) - Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks.
LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning.
We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
arXiv Detail & Related papers (2024-06-20T13:08:09Z) - Efficient Prompting for LLM-based Generative Internet of Things [88.84327500311464]
Large language models (LLMs) have demonstrated remarkable capacities on various tasks.
We propose a text-based generative IoT (GIoT) system deployed in the local network setting.
arXiv Detail & Related papers (2024-06-14T19:24:00Z) - Reinforcement Learning Problem Solving with Large Language Models [0.0]
Large Language Models (LLMs) have an extensive amount of world knowledge, and this has enabled their application in various domains to improve the performance of Natural Language Processing (NLP) tasks.
This has also facilitated a more accessible paradigm of conversation-based interactions between humans and AI systems to solve intended problems.
We show the practicality of our approach through two detailed case studies for "Research Scientist" and "Legal Matter Intake"
arXiv Detail & Related papers (2024-04-29T12:16:08Z) - Wiki-LLaVA: Hierarchical Retrieval-Augmented Generation for Multimodal LLMs [39.54891426369773]
We focus on endowing such models with the capability of answering questions that require external knowledge.
Our approach, termed Wiki-LLaVA, aims at integrating an external knowledge source of multimodal documents.
We conduct extensive experiments on datasets tailored for visual question answering with external data and demonstrate the appropriateness of our approach.
arXiv Detail & Related papers (2024-04-23T18:00:09Z) - Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks.
The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human.
These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
arXiv Detail & Related papers (2024-01-30T17:38:54Z) - Large Language Models for Generative Information Extraction: A Survey [89.71273968283616]
Information extraction aims to extract structural knowledge from plain natural language texts.
generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation.
LLMs offer viable solutions for IE tasks based on a generative paradigm.
arXiv Detail & Related papers (2023-12-29T14:25:22Z) - LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language
Models [56.25156596019168]
This paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for large language models (LLMs)
Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games.
arXiv Detail & Related papers (2023-11-30T03:59:31Z) - Exploring the Potential of Large Language Models in Computational Argumentation [54.85665903448207]
Large language models (LLMs) have demonstrated impressive capabilities in understanding context and generating natural language.
This work aims to embark on an assessment of LLMs, such as ChatGPT, Flan models, and LLaMA2 models, in both zero-shot and few-shot settings.
arXiv Detail & Related papers (2023-11-15T15:12:15Z) - Exploring the Integration of Large Language Models into Automatic Speech
Recognition Systems: An Empirical Study [0.0]
This paper explores the integration of Large Language Models (LLMs) into Automatic Speech Recognition (ASR) systems.
Our primary focus is to investigate the potential of using an LLM's in-context learning capabilities to enhance the performance of ASR systems.
arXiv Detail & Related papers (2023-07-13T02:31:55Z) - Frugal Prompting for Dialog Models [17.048111072193933]
This study examines different approaches for building dialog systems using large language models (LLMs)
As part of prompt tuning, we experiment with various ways of providing instructions, exemplars, current query and additional context.
The research also analyzes the representations of dialog history that have the optimal usable-information density.
arXiv Detail & Related papers (2023-05-24T09:06:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.