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    <abstract>Recent advancements in large language models (LLMs) have enabled more autonomous conversational AI agents. However, challenges remain in developing effective chatbots, particularly in addressing LLMs’ lack of “statefulness”. This paper presents Converso, a novel chatbot framework that introduces a new conversation flow based on stateful conversational forms designed for natural data acquisition through dialogue. Converso leverages LLMs, LangChain, and a containerized architecture to provide an end-to-end chatbot system with Telegram as the user interface. The key innovation in Converso is its implementation of conversational forms, which guide users through form completion via a structured dialogue flow. Converso’s chatbots can be linked with multiple forms that are automatically triggered based on the user’s intent. Our forms are fully integrated into the LangChain ecosystem, allowing the LLM to use tools for form completion and dynamic validation. Evaluations show that this approach significantly improves task completion rates compared to LLMs alone. Converso demonstrates how specifically designed conversational flows can enhance the capabilities of LLM-based chatbots for practical data collection applications. Our implementation is available at: https://github.com/gianfrancodemarco/converso-chatbot.</abstract>
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%0 Conference Proceedings
%T Converso: Improving LLM Chatbot Interfaces and Task Execution via Conversational Form
%A Demarco, Gianfranco
%A Fanelli, Nicola
%A Vessio, Gennaro
%A Castellano, Giovanna
%Y Sousa-Silva, Rui
%Y Lopes Cardoso, Henrique
%Y Koponen, Maarit
%Y Pareja Lora, Antonio
%Y Seresi, Márta
%S Proceedings of the 1st LUHME Workshop
%D 2024
%8 October
%I CLUP, Centro de Linguística da Universidade do Porto FLUP - Faculdade de Letras da Universidade do Porto
%C Santiago de Compostela, Spain
%F demarco-etal-2024-converso
%X Recent advancements in large language models (LLMs) have enabled more autonomous conversational AI agents. However, challenges remain in developing effective chatbots, particularly in addressing LLMs’ lack of “statefulness”. This paper presents Converso, a novel chatbot framework that introduces a new conversation flow based on stateful conversational forms designed for natural data acquisition through dialogue. Converso leverages LLMs, LangChain, and a containerized architecture to provide an end-to-end chatbot system with Telegram as the user interface. The key innovation in Converso is its implementation of conversational forms, which guide users through form completion via a structured dialogue flow. Converso’s chatbots can be linked with multiple forms that are automatically triggered based on the user’s intent. Our forms are fully integrated into the LangChain ecosystem, allowing the LLM to use tools for form completion and dynamic validation. Evaluations show that this approach significantly improves task completion rates compared to LLMs alone. Converso demonstrates how specifically designed conversational flows can enhance the capabilities of LLM-based chatbots for practical data collection applications. Our implementation is available at: https://github.com/gianfrancodemarco/converso-chatbot.
%U https://aclanthology.org/2024.luhme-1.1/
%P 5-11

Markdown (Informal)

[Converso: Improving LLM Chatbot Interfaces and Task Execution via Conversational Form](https://aclanthology.org/2024.luhme-1.1/) (Demarco et al., LUHME 2024)

• Converso: Improving LLM Chatbot Interfaces and Task Execution via Conversational Form (Demarco et al., LUHME 2024)

ACL

• Gianfranco Demarco, Nicola Fanelli, Gennaro Vessio, and Giovanna Castellano. 2024. Converso: Improving LLM Chatbot Interfaces and Task Execution via Conversational Form. In Proceedings of the 1st LUHME Workshop, pages 5–11, Santiago de Compostela, Spain. CLUP, Centro de Linguística da Universidade do Porto FLUP - Faculdade de Letras da Universidade do Porto.