Can LLMs Generate High-Quality Task-Specific Conversations?

ACL ARR 2026 January Submission6921 Authors

Keywords: Dialogue and Interactive Systems, Generation, Resources and Evaluation

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.

Paper Type: Long

Research Area: Natural Language Generation

Research Area Keywords: Dialogue and Interactive Systems, Generation, Resources and Evaluation

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data analysis

Languages Studied: English

Submission Number: 6921