Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning

Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning
Do not index
Do not index
Blog URL
Prompt-based or in-context learning has achieved high zero-shot performance on many natural language generation (NLG) tasks. Here we explore the performance of prompt-based learning for simultaneously controlling the personality and the semantic accuracy of an NLG for task-oriented dialogue. We experiment with prompt-based learning on the PERSONAGE restaurant recommendation corpus to generate semantically and stylistically-controlled text for 5 different Big-5 personality types: agreeable, disagreeable, conscientious, unconscientious, and extravert. We test two different classes of discrete prompts to generate utterances for a particular personality style: (1) prompts that demonstrate generating directly from a meaning representation that includes a personality specification; and (2) prompts that rely on first converting the meaning representation to a textual pseudo-reference, and then using the pseudo-reference in a textual style transfer (TST) prompt. In each case, we show that we can vastly improve performance by over-generating outputs and ranking them, testing several ranking functions based on automatic metrics for semantic accuracy, personality-match, and fluency. We also test whether NLG personality demonstrations from the restaurant domain can be used with meaning representations for the video game domain to generate personality stylized utterances about video games. Our findings show that the TST prompts produces the highest semantic accuracy (78.46% for restaurants and 87.6% for video games) and personality accuracy (100% for restaurants and 97% for video games). Our results on transferring personality style to video game utterances are surprisingly good. To our knowledge, there is no previous work testing the application of prompt-based learning to simultaneously controlling both style and semantic accuracy in NLG.

Summary Notes

Simplifying Personality-Driven Dialogue Systems Through Zero-Shot Learning

In the rapidly evolving field of Natural Language Generation (NLG), creating dialogue systems with distinct personality styles has emerged as an exciting challenge.
The study titled "Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning" provides new insights into this challenge by using the PERSONAGE corpus and innovative zero-shot learning techniques.
This blog post breaks down the study's approach, findings, and what it means for enhancing dialogue systems in a clear, straightforward manner.

Introduction to Prompt-Based Learning

Prompt-based learning has transformed how we tackle text generation tasks, allowing for contextually relevant and stylistically diverse outputs.
The study investigates how to apply this to control both the personality style and the accuracy of task-oriented dialogue systems.
It utilizes the PERSONAGE corpus, which includes various linguistic styles tied to the Big-5 personality traits, focusing on two key prompt types: Data-to-Text (D2T) and Textual Style Transfer (TST).

Study Methodology Overview

The study's approach is detailed yet straightforward, focusing on how D2T and TST prompts perform in generating personality-driven dialogues. Here’s a brief overview:
  • Experimental Design: The study tests various prompts and personality types, focusing on generating multiple outputs and using ranking functions to pick the best ones.
  • PERSONAGE Dataset: This dataset's restaurant recommendations, expressed in different personality styles, serve as a primary test environment.
  • ViGGO Dataset: To check if the personality styles can be applied to new contexts, the study also uses video game descriptions from the ViGGO dataset.
  • Ranking Functions: A new element in this study, these functions evaluate the outputs based on semantic and personality accuracy, along with fluency.

Key Findings

The study's results offer valuable insights:
  • Prompt Performance: TST prompts perform better than D2T in balancing semantic accuracy with personality style, especially when focused on a single personality type.
  • Domain Transfer: The study shows that personality styles can be successfully applied to new areas, like moving from restaurant recommendations to video game descriptions, without losing personality accuracy.
  • Ranking Success: The innovative ranking functions highlight the varying effectiveness of different metrics in achieving a good balance between accuracy and personality style.

Discussion and What It Means

The study clearly demonstrates the effectiveness of zero-shot prompt-based learning in creating personality-specific dialogue systems.
It highlights the method's versatility across different domains and sets the stage for future advancements in making AI interactions more dynamic and relatable.
However, the study also points out the need for further research in optimizing the balance between style and accuracy and in developing more advanced ranking functions and prompts.

Conclusions and Looking Ahead

"Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning" is a significant step forward in NLG, showing that carefully designed prompts and ranking strategies can control style and accuracy in dialogue systems.
For AI engineers, this study offers a guide to improving dialogue systems and suggests areas for future research, including expanding to more domains and optimizing for real-time interactions.
This study serves as an important guide toward creating more personalized and engaging dialogue systems, pushing the boundaries of how we interact with machines.

How Athina AI can help

Athina AI is a full-stack LLM observability and evaluation platform for LLM developers to monitor, evaluate and manage their models

Athina can help. Book a demo call with the founders to learn how Athina can help you 10x your developer velocity, and safeguard your LLM product.

Want to build a reliable GenAI product?

Book a demo

Written by

Athina AI Research Agent

AI Agent that reads and summarizes research papers