# Single- vs. Dual-Prompt Dialogue Generation with LLMs for Job Interviews in Human Resources

arXiv: https://arxiv.org/abs/2502.18650 · HTML: https://mlsanity.com/paper/2502.18650/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 23 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators](https://mlsanity.com/paper/2404.04475/) (2024)
- [LLM Evaluators Recognize and Favor Their Own Generations](https://mlsanity.com/paper/2404.13076/) (2024)
- [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://mlsanity.com/paper/2212.10465/) (2022)
- [Does Prompt Formatting Have Any Impact on LLM Performance?](https://mlsanity.com/paper/2411.10541/) (2024)
- [Imprompter: Tricking LLM Agents into Improper Tool Use](https://mlsanity.com/paper/2410.14923/) (2024)
- [A Comprehensive Analysis of the Effectiveness of Large Language Models as Automatic Dialogue Evaluators](https://mlsanity.com/paper/2312.15407/) (2023)
- [BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn Dialogues](https://mlsanity.com/paper/2310.13650/) (2023)
- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [DiaSynth: Synthetic Dialogue Generation Framework for Low Resource Dialogue Applications](https://mlsanity.com/paper/2409.19020/) (2024)
- [Tuning LLM Judge Design Decisions for 1/1000 of the Cost](https://mlsanity.com/paper/2501.17178/) (2025)
- [Leveraging LLMs for Dialogue Quality Measurement](https://mlsanity.com/paper/2406.17304/) (2024)

## Built on by (most cited first, 2 of 2)

- [APIDA-Chat: Structured Synthesis of API Search Dialogues to Bootstrap Conversational Agents](https://mlsanity.com/paper/2510.03743/) (2025)
- [LLM Nepotism in Organizational Governance](https://mlsanity.com/paper/2604.09620/) (2026)

## Abstract (arXiv)

Optimizing language models for use in conversational agents requires large quantities of example dialogues. Increasingly, these dialogues are synthetically generated by using powerful large language models (LLMs), especially in domains where obtaining authentic human data is challenging. One such domain is human resources (HR). In this context, we compare two LLM-based dialogue generation methods for producing HR job interviews, and assess which method generates higher-quality dialogues, i.e., those more difficult to distinguish from genuine human discourse. The first method uses a single prompt to generate the complete interview dialogue. The second method uses two agents that converse with each other. To evaluate dialogue quality under each method, we ask a judge LLM to determine whether AI was used for interview generation, using pairwise interview comparisons. We empirically find that, at the expense of a sixfold increase in token count, interviews generated with the dual-prompt method achieve a win rate 2 to 10 times higher than those generated with the single-prompt method. This difference remains consistent regardless of whether GPT-4o or Llama 3.3 70B is used for either interview generation or quality judging.