Large language models for behavioral modeling: A literature survey
- URL: http://arxiv.org/abs/2509.24782v1
- Date: Mon, 29 Sep 2025 13:43:02 GMT
- Title: Large language models for behavioral modeling: A literature survey
- Abstract summary: Large language models (LLMs) have been extensively utilized for behavioral modeling.<n>This study aims to provide an overview of the existing research on the use of LLMs for behavioral modeling.
- Score: 0.5076419064097734
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In recent years, large language models (LLMs) have been extensively utilized for behavioral modeling, for example, to automatically generate sequence diagrams. However, no overview of this work has been published yet. Such an overview will help identify future research directions and inform practitioners and educators about the effectiveness of LLMs in assisting behavioral modeling. This study aims to provide an overview of the existing research on the use of LLMs for behavioral modeling, particularly focusing on use case and sequence diagrams. Through a term-based search, we filtered and identified 14 relevant primary studies. Our analysis of the selected primary studies reveals that LLMs have demonstrated promising results in automatically generating use case and sequence diagrams. In addition, we found that most of the current literature lacks expert-based evaluations and has mainly used GPT-based models. Therefore, future work should evaluate a broader range of LLMs for behavioral modeling and involve domain experts to evaluate the output of LLMs.
Related papers
- So Many Opinions, So Many LLMs: Comparing Large Language Models to Traditional Machine Learning for Open- Ended Survey Analysis [0.0]
Open-ended surveys offer valuable insights, but they are notoriously difficult to analyze at scale.<n>This study investigates how different large language models (LLMs) understand and analyze NSSE open-ended survey responses.
arXiv Detail & Related papers (2026-05-04T00:38:59Z) - Applying Large Language Models to Travel Satisfaction Analysis [2.5105418815378555]
This study uses household survey data collected in Shanghai to identify the existence and source of misalignment between Large Language Models (LLMs) and humans.<n>LLMs have strongcapabilities in contextual understanding and generalization, significantly reducing dependence on task-specific data.<n>We propose an LLM-based modeling approach that can be applied to model travel behavior with small sample sizes.
arXiv Detail & Related papers (2025-05-29T09:11:58Z) - Efficient Model Selection for Time Series Forecasting via LLMs [52.31535714387368]
We propose to leverage Large Language Models (LLMs) as a lightweight alternative for model selection.<n>Our method eliminates the need for explicit performance matrices by utilizing the inherent knowledge and reasoning capabilities of LLMs.
arXiv Detail & Related papers (2025-04-02T20:33:27Z) - Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMs [0.464982780843177]
This work investigates the ability of open Large Language Models (LLMs) to predict citation intent through in-context learning and fine-tuning.<n>We evaluate twelve model variations across five prominent open LLM families using zero-, one-, few-, and many-shot prompting.<n>We then demonstrate the significant impact of task-specific adaptation by fine-tuning this model, achieving a relative F1-score improvement of 8% on the SciCite dataset and 4.3% on the ACL-ARC dataset compared to the instruction-tuned baseline.
arXiv Detail & Related papers (2025-02-20T13:45:42Z) - From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations.<n>This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
arXiv Detail & Related papers (2025-02-17T12:58:17Z) - Applying Large Language Models in Knowledge Graph-based Enterprise Modeling: Challenges and Opportunities [0.0]
Large language models (LLMs) in enterprise modeling have recently started to shift from academic research to that of industrial applications.<n>In this paper we employ a knowledge graph-based approach for enterprise modeling and investigate the potential benefits of LLMs.
arXiv Detail & Related papers (2025-01-07T06:34:17Z) - MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs [97.94579295913606]
Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia.<n>In the development process, evaluation is critical since it provides intuitive feedback and guidance on improving models.<n>This work aims to offer researchers an easy grasp of how to effectively evaluate MLLMs according to different needs and to inspire better evaluation methods.
arXiv Detail & Related papers (2024-11-22T18:59:54Z) - PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics [51.17512229589]
PoLLMgraph is a model-based white-box detection and forecasting approach for large language models.
We show that hallucination can be effectively detected by analyzing the LLM's internal state transition dynamics.
Our work paves a new way for model-based white-box analysis of LLMs, motivating the research community to further explore, understand, and refine the intricate dynamics of LLM behaviors.
arXiv Detail & Related papers (2024-04-06T20:02:20Z) - Exploring the Potential of Large Language Models in Graph Generation [51.046188600990014]
Graph generation requires large language models (LLMs) to generate graphs with given properties.
This paper explores the abilities of LLMs for graph generation with systematical task designs and experiments.
Our evaluations demonstrate that LLMs, particularly GPT-4, exhibit preliminary abilities in graph generation tasks.
arXiv Detail & Related papers (2024-03-21T12:37:54Z) - Towards Modeling Learner Performance with Large Language Models [7.002923425715133]
This paper investigates whether the pattern recognition and sequence modeling capabilities of LLMs can be extended to the domain of knowledge tracing.
We compare two approaches to using LLMs for this task, zero-shot prompting and model fine-tuning, with existing, non-LLM approaches to knowledge tracing.
While LLM-based approaches do not achieve state-of-the-art performance, fine-tuned LLMs surpass the performance of naive baseline models and perform on par with standard Bayesian Knowledge Tracing approaches.
arXiv Detail & Related papers (2024-02-29T14:06:34Z) - The Emergence of Large Language Models in Static Analysis: A First Look
through Micro-Benchmarks [3.848607479075651]
We investigate the role that current Large Language Models (LLMs) can play in improving callgraph analysis and type inference for Python programs.
Our study reveals that LLMs show promising results in type inference, demonstrating higher accuracy than traditional methods, yet they exhibit limitations in callgraph analysis.
arXiv Detail & Related papers (2024-02-27T16:53:53Z) - Large Language Models for Data Annotation and Synthesis: A Survey [49.8318827245266]
This survey focuses on the utility of Large Language Models for data annotation and synthesis.<n>It includes an in-depth taxonomy of data types that LLMs can annotate, a review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation and synthesis.
arXiv Detail & Related papers (2024-02-21T00:44:04Z)
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.