Using Prompts to Guide Large Language Models in Imitating a Real Person's Language Style (2410.03848v1)

Abstract: 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 LLMs under the guidance of the same zero-shot prompt. It also involves comparing the imitation ability of the same LLM 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.

LLMs and Language Style Imitation Through Prompt Engineering

The paper, titled "Using Prompts to Guide LLMs in Imitating a Real Person's Language Style" by Ziyang Chen and Stylios Moscholios, investigates the capabilities of LLMs such as GPT-4, Llama 3, and Gemini 1.5 in mimicking the language style of real individuals using prompt engineering techniques. This paper explores the efficacy of different prompting methods, including Zero-Shot, Chain-of-Thought (CoT), and Tree-of-Thoughts (ToT), to enhance the language style imitation of LLMs without altering their core parameters.

Methodology Overview

The research is structured into three primary tasks:

1. Comparative Evaluation of LLMs: The first task involves assessing the language style imitation capabilities of three LLMs under a uniform zero-shot prompt. This involves generating dialogues that emulate the styles of public figures like Elon Musk and Tom Holland, anonymized as 'Mark1' and 'Tony', respectively. The paper uses datasets derived from celebrity interviews to perform this evaluation.
2. Impact of Different Prompt Types: The second task compares the effectiveness of different prompting techniques—Zero-Shot, CoT, and ToT—on Llama 3 while emulating the language style of 'Mark2'. This task investigates how nuanced prompting can elevate a model's stylistic mimicry.
3. Development of Conversational AI: Utilizing the ToT framework, the final task develops a conversational AI that interacts using the language style of a specific individual, demonstrating practical application in AI and digital human technologies.

Evaluation Techniques

The paper employs three distinct evaluation methodologies:

• Human Evaluation: Experts rate generated conversations based on criteria like word choice and sentence structure.
• LLM Evaluation: Claude 3.5 is used to automatically assess the similarity of generated content to the target style.
• Automatic Evaluation: A binary classifier, trained through authorship attribution techniques, predicts whether a text segment belongs to the target individual, quantifying the imitation success rate.

Results and Analysis

The paper yields several key insights:

• Effectiveness of Llama 3: Llama 3 consistently outperforms GPT-4 and Gemini 1.5 in language style imitation under the same prompting conditions.
• Impact of Prompt Types: The ToT prompting method significantly enhances Llama 3's ability to replicate language styles compared to other prompting types, indicating the utility of structured, multi-step reasoning frameworks.
• Conversational AI Application: The implementation of ToT in conversational AI demonstrates that LLMs can effectively mimic individual language styles for interactive applications without extensive retraining.

Implications and Future Directions

The implications of this research are broad in both practical and theoretical domains. Practically, developing conversational AI that mimics specific human language styles could revolutionize virtual assistants, making interactions more personalized and human-like. Theoretically, it provides insights into the potential and limitations of different prompting strategies in augmenting LLM performance.

Future developments in AI could explore a wider range of LLMs and prompting techniques, delving deeper into optimizing language style imitation without relying on large datasets. This could enhance the integration of LLMs in digital human technology and AI cloning, expanding their utility in simulating authentic human interactions.

In conclusion, this paper highlights the significant role of prompt engineering in enhancing LLMs' ability to imitate personal language styles. It opens new avenues for efficient, cost-effective solutions in the landscape of conversational AI and digital human technologies.