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

• URL: http://arxiv.org/abs/2502.18650v1
• Date: Tue, 25 Feb 2025 21:19:27 GMT
• Title: Single- vs. Dual-Prompt Dialogue Generation with LLMs for Job Interviews in Human Resources
• Authors: Joachim De Baer, A. Seza Doğruöz, Thomas Demeester, Chris Develder,
• Abstract summary: We compare two methods for dialogue generation for human resources job interviews.<n>We find that interviews generated with the dual-prompt method achieve a win rate up to ten times higher than those generated with the single-prompt method.<n>This difference remains consistent regardless of whether GPT-4o or Llama 3.3 70B is used for either interview generation or judging quality.
• Score: 11.083396379885478
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 with challenges to obtain authentic human data. One such domain is human resources (HR). In this context, we compare two LLM-based dialogue generation methods for the use case of generating HR job interviews, and assess whether one method generates higher-quality dialogues that are more challenging to distinguish from genuine human discourse. The first method uses a single prompt to generate the complete interview dialog. 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 demonstrate that despite a sixfold increase in token cost, interviews generated with the dual-prompt method achieve a win rate up to ten 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 judging quality.

Related papers

• Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts [19.73376945990922]
  We introduce a bottom-up conversation synthesis approach, where QA pairs are generated first and then combined into a coherent dialogue. This structure allows the use of non-local models in stages that do not involve proprietary knowledge. Both human and automated evaluations demonstrate that our approach produces more realistic and higher-quality dialogues.
  arXiv  Detail & Related papers  (2025-04-19T18:25:53Z)
• Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
  Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion. We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations. Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
  arXiv  Detail & Related papers  (2024-11-07T21:37:51Z)
• Self-Directed Turing Test for Large Language Models [56.64615470513102]
  The Turing test examines whether AIs can exhibit human-like behaviour in natural language conversations. Traditional Turing tests adopt a rigid dialogue format where each participant sends only one message each time. This paper proposes the Self-Directed Turing Test, which extends the original test with a burst dialogue format.
  arXiv  Detail & Related papers  (2024-08-19T09:57:28Z)
• Synthetic Dialogue Dataset Generation using LLM Agents [7.933485970511388]
  We develop two agents that "talk" to each other, one acting as the conversational agent, and the other acting as the user. Using a set of text descriptions of linear problems from NL4Opt available to the user only, the agent and the user engage in conversation until the agent has retrieved all key information from the original problem description. We conduct human and automatic evaluations, including an evaluation approach that uses GPT-4 to mimic the human evaluation metrics.
  arXiv  Detail & Related papers  (2024-01-30T21:49:30Z)
• Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations [70.7884839812069]
  Large language models (LLMs) have emerged as powerful and general solutions to many natural language tasks. However, many of the most important applications of language generation are interactive, where an agent has to talk to a person to reach a desired outcome. In this work, we explore a new method for adapting LLMs with RL for such goal-directed dialogue.
  arXiv  Detail & Related papers  (2023-11-09T18:45:16Z)
• BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn Dialogues [72.65163468440434]
  This report provides a preliminary evaluation of existing large language models for human-style multi-turn chatting. We prompt large language models (LLMs) to generate a full multi-turn dialogue based on the ChatSEED, utterance by utterance. We find GPT-4 can generate human-style multi-turn dialogues with impressive quality, significantly outperforms its counterparts.
  arXiv  Detail & Related papers  (2023-10-20T16:53:51Z)
• Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs [59.74002011562726]
  We propose a novel linguistic cue-based chain-of-thoughts (textitCue-CoT) to provide a more personalized and engaging response. We build a benchmark with in-depth dialogue questions, consisting of 6 datasets in both Chinese and English. Empirical results demonstrate our proposed textitCue-CoT method outperforms standard prompting methods in terms of both textithelpfulness and textitacceptability on all datasets.
  arXiv  Detail & Related papers  (2023-05-19T16:27:43Z)
• GRASP: Guiding model with RelAtional Semantics using Prompt [3.1275060062551208]
  We propose a Guiding model with RelAtional Semantics using Prompt (GRASP) We adopt a prompt-based fine-tuning approach and capture relational semantic clues of a given dialogue with an argument-aware prompt marker strategy. In the experiments, GRASP state-of-the-art performance in terms of both F1 and F1c scores on a DialogRE dataset.
  arXiv  Detail & Related papers  (2022-08-26T08:19:28Z)
• Smoothing Dialogue States for Open Conversational Machine Reading [70.83783364292438]
  We propose an effective gating strategy by smoothing the two dialogue states in only one decoder and bridge decision making and question generation. Experiments on the OR-ShARC dataset show the effectiveness of our method, which achieves new state-of-the-art results.
  arXiv  Detail & Related papers  (2021-08-28T08:04:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.