mlsanityData to 14 Oct 2026

Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style

arXiv 2410.03848 ↗First posted 4 Oct 2024cs.CL cs.CVcited by 6

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Abstract (arXiv)

Large language models (LLMs), such as GPT series and Llama series have demonstrated strong capabilities in natural language processing, contextual understanding, and text generation. In recent years, researchers are trying to enhance the abilities of LLMs in performing various tasks, and numerous studies have proved that well-designed prompts can significantly improve the performance of LLMs on these tasks. This study compares the language style imitation ability of three different large language models under the guidance of the same zero-shot prompt. It also involves comparing the imitation ability of the same large language model when guided by three different prompts individually. Additionally, by applying a Tree-of-Thoughts (ToT) Prompting method to Llama 3, a conversational AI with the language style of a real person was created. In this study, three evaluation methods were used to evaluate LLMs and prompts. The results show that Llama 3 performs best at imitating language styles, and that the ToT prompting method is the most effective to guide it in imitating language styles. Using a ToT framework, Llama 3 was guided to interact with users in the language style of a specific individual without altering its core parameters, thereby creating a text-based conversational AI that reflects the language style of the individual.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 32 of its 52 references that are in the map. First posted on arXiv 4 Oct 2024.

Builds on

32 of 52 references.

References · 32

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  3. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 431
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20 more
  1. 2022A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Modelscited by 129
  2. 2023ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challengescited by 110
  3. 2024The 6th Affective Behavior Analysis in-the-wild (ABAW) Competitioncited by 78
  4. 2020Deep Transparent Prediction through Latent Representation Analysiscited by 72
  5. 2024Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyondcited by 60
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  9. 2023A Deep Neural Architecture for Harmonizing 3-D Input Data Analysis and Decision Making in Medical Imagingcited by 39
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  11. 2024Domain adaptation, Explainability & Fairness in AI for Medical Image Analysis: Diagnosis of COVID-19 based on 3-D Chest CT-scanscited by 30
  12. 2024COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI Systemcited by 23
  13. 2023BTDNet: a Multi-Modal Approach for Brain Tumor Radiogenomic Classificationcited by 20
  14. 2023Who Wrote it and Why? Prompting Large-Language Models for Authorship Verificationcited by 19
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  16. 2024Uncertainty-guided Contrastive Learning for Single Source Domain Generalisationcited by 14
  17. 2024SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detectioncited by 14
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

  1. 2025Personalized Text Generation with Contrastive Activation Steeringcited by 34
  2. 2025Catch Me If You Can? Not Yet: LLMs Still Struggle to Imitate the Implicit Writing Styles of Everyday Authorscited by 10
  3. 2025Deep-Bench: Deep Learning Benchmark Dataset for Code Generationcited by 3
  4. 2025How Well Do LLMs Imitate Human Writing Style?cited by 2
  5. 2025Implementing Long Text Style Transfer with LLMs through Dual-Layered Sentence and Paragraph Structure Extraction and Mappingcited by 1
  6. 2026Authorship Impersonation via LLM Prompting does not Evade Authorship Verification Methodscited by 0