PSYDIAL: A Novel Pipeline for Generating Personality-Based Synthetic Dialogues Using Large Language Models

Concetti Chiave

A novel end-to-end pipeline for generating personality-based synthetic dialogue data using prompting techniques with large language models.

Sintesi

The paper presents a five-step pipeline for generating personality-based synthetic dialogue data using large language models (LLMs) like CHATGPT. The key steps are:

Personality Setting: Assigning specific personality traits (extraversion or introversion) to the two interlocutors in the dialogue.

Profile Selecting: Selecting a profile sentence from the PERSONA-CHAT dataset that aligns with the assigned personality.

Dialogue Generation: Generating the dialogue using a prompt that includes the profile, personality, character, and style information.

Dialogue Filtering: Evaluating the generated dialogues to ensure they match the specified personality, profile, and style using LLM-based filtering.

Dialogue Regeneration: Iteratively regenerating any dialogues that do not pass the filtering criteria.

The authors create the PSYDIAL dataset, the first Korean personality-based dialogue dataset, using this pipeline. Experiments show that models fine-tuned on PSYDIAL significantly outperform pre-trained models and those fine-tuned on a generic chit-chat dataset in generating responses that reflect personality.

Statistiche

The PSYDIAL dataset contains around 2,900 dialogues.
On average, each dialogue has 8 turns and each utterance has 33 syllables.

Citazioni

"We present a novel end-to-end personality-based synthetic dialogue data generation pipeline, specifically designed to elicit responses from large language models via prompting."
"Notably, we focus on the Extraversion dimension of the Big Five personality model in our research."
"Experimental results indicate that while pre-trained models and those fine-tuned with a chit-chat dataset struggle to generate responses reflecting personality, models trained with PSYDIAL show significant improvements."

Approfondimenti chiave tratti da

PSYDIAL

by Ji-Eun Han,J... alle arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00930.pdf

Domande più approfondite

How can this pipeline be extended to generate dialogues for other personality dimensions beyond extraversion?

To extend this pipeline to generate dialogues for other personality dimensions beyond extraversion, we can follow a similar process with slight modifications. Firstly, we would need to define specific statements or descriptions for each additional personality dimension, similar to the ones provided for extraversion in the context. These statements should capture the essence of each personality trait, such as openness, conscientiousness, agreeableness, and neuroticism in the Big Five model.
Next, in the Personality Setting phase, we would randomly select statements related to the specific personality dimension being focused on. This ensures that the model understands and embodies the chosen personality trait accurately. The Profile Selecting phase can remain the same, but with profiles that align with the new personality dimensions.
During the Dialogue Generation phase, the Personality Prompt would include descriptions for the new personality dimensions, allowing the model to generate dialogues reflecting these traits. The Character Prompt and Style Prompt can also be adjusted to suit the characteristics associated with the new personality dimensions.
Overall, by customizing the pipeline with relevant prompts and descriptions for each personality dimension, we can effectively generate dialogues that reflect a wide range of personality traits beyond just extraversion.

How can the potential challenges in applying this approach to other languages beyond Korean be addressed?

When applying this approach to other languages beyond Korean, several challenges may arise, such as language nuances, cultural differences, and linguistic variations. To address these challenges, the following strategies can be implemented:

Language Adaptation: Translate the personality descriptions, prompts, and dialogue samples into the target language to ensure cultural relevance and linguistic accuracy.

Cultural Considerations: Conduct thorough research on the cultural norms and communication styles of the target language speakers to tailor the prompts and dialogues accordingly.

Data Collection: Gather language-specific dialogue datasets to fine-tune the models and ensure that the generated dialogues are contextually appropriate and natural-sounding.

Model Training: Fine-tune the language models on the specific language data to capture the linguistic nuances and intricacies of the target language, improving the quality of the generated dialogues.

Validation and Feedback: Seek feedback from native speakers of the target language to validate the realism and coherence of the generated dialogues, making necessary adjustments based on their input.

By addressing these challenges through language adaptation, cultural considerations, data collection, model training, and validation processes, the approach can be effectively applied to generate personality-based dialogues in languages other than Korean.

How can the generated dialogues be further validated for their realism and coherence through human evaluation?

To validate the realism and coherence of the generated dialogues through human evaluation, the following steps can be taken:

Expert Review: Engage language experts or linguists proficient in the target language to assess the dialogues for linguistic accuracy, naturalness, and cultural appropriateness.

User Testing: Conduct user testing with a diverse group of individuals representing the target audience to gather feedback on the dialogues' clarity, relevance, and overall quality.

Scalability Testing: Evaluate the scalability of the dialogues by testing them in various scenarios and contexts to ensure that they remain coherent and realistic across different settings.

Feedback Mechanism: Implement a feedback mechanism where users can provide comments, suggestions, and ratings on the generated dialogues to continuously improve their quality.

Quantitative Analysis: Use metrics such as fluency, coherence, and engagement to quantitatively assess the dialogues' quality and compare them against benchmarks for objective evaluation.

By combining expert review, user testing, scalability testing, feedback mechanisms, and quantitative analysis, the generated dialogues can be thoroughly validated for their realism and coherence through human evaluation.