Can LLMs Generate High-Quality Task-Specific Conversations?

TL;DR

This paper presents a parameterization framework for controlling and improving the quality of task-specific conversations generated by large language models, enabling precise dialogue property specification.

Contribution

It introduces a novel parameter-based control method for conversation quality in LLMs, addressing coherence, consistency, and granularity challenges.

Findings

Parameter control significantly affects conversation properties

Framework improves topic coherence and character consistency

Applicable across various dialogue applications

Abstract

This paper introduces a parameterization framework for controlling conversation quality in large language models. We explore nine key parameters across six dimensions that enable precise specification of dialogue properties. Through experiments with state-of-the-art LLMs, we demonstrate that parameter-based control produces statistically significant differences in generated conversation properties. Our approach addresses challenges in conversation generation, including topic coherence, knowledge progression, character consistency, and control granularity. The framework provides a standardized method for conversation quality control with applications in education, therapy, customer service, and entertainment. Future work will focus on implementing additional parameters through architectural modifications and developing benchmark datasets for evaluation.