Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk Article Swipe

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Dennis Ulmer , Elman Mansimov , Kaixiang Lin , Justin Sun , Xibin Gao , Yi Zhang ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2401.05033

Large language models (LLMs) are powerful dialogue agents, but specializing them towards fulfilling a specific function can be challenging. Instructing tuning, i.e. tuning models on instruction and sample responses generated by humans (Ouyang et al., 2022), has proven as an effective method to do so, yet requires a number of data samples that a) might not be available or b) costly to generate. Furthermore, this cost increases when the goal is to make the LLM follow a specific workflow within a dialogue instead of single instructions. Inspired by the self-play technique in reinforcement learning and the use of LLMs to simulate human agents, we propose a more effective method for data collection through LLMs engaging in a conversation in various roles. This approach generates a training data via "self-talk" of LLMs that can be refined and utilized for supervised fine-tuning. We introduce an automated way to measure the (partial) success of a dialogue. This metric is used to filter the generated conversational data that is fed back in LLM for training. Based on our automated and human evaluations of conversation quality, we demonstrate that such self-talk data improves results. In addition, we examine the various characteristics that showcase the quality of generated dialogues and how they can be connected to their potential utility as training data.

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Conversation

Computer Science

Reinforcement Learning

Artificial Intelligence

Performance Indicator

Human–Computer Interaction

Communication

Operations Management

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Conversation Computer science Bootstrapping (finance) Workflow Reinforcement learning Quality (philosophy) Artificial intelligence Sample (material) Metric (unit) Task (project management) Human–computer interaction Psychology Communication Operations management Philosophy Database Epistemology Management Chromatography Economics Chemistry Financial economics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2401.05033
PDF: https://arxiv.org/pdf/2401.05033
OA Status: green
Cited By: 3
References: 44
Related Works: 10
OpenAlex ID: https://openalex.org/W4390832445

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DOI: https://doi.org/10.48550/arxiv.2401.05033

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Title: Bootstrapping LLM-based Task-Oriented Dialogue Agents via Self-Talk

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-01-10

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Authors: Dennis Ulmer, Elman Mansimov, Kaixiang Lin, Justin Sun, Xibin Gao, Yi Zhang

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Landing page: https://arxiv.org/abs/2401.05033

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PDF URL: https://arxiv.org/pdf/2401.05033

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Concepts: Conversation, Computer science, Bootstrapping (finance), Workflow, Reinforcement learning, Quality (philosophy), Artificial intelligence, Sample (material), Metric (unit), Task (project management), Human–computer interaction, Psychology, Communication, Operations management, Philosophy, Database, Epistemology, Management, Chromatography, Economics, Chemistry, Financial economics

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Cited by: 3

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