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        <title>Utilizing Large Language Models for Focused Conversational Assistants</title>
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        <namePart type="given">Shruti</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>A focused conversational assistant (FCA) realizes human-computer interaction bounded in a predefined scope of operation. With the advent of large language models (LLMs), it has become imperative to integrate them in conversational assistants (CAs). However, an LLM can become largely inaccurate in an FCA with multiple responsibilities, like information extraction, scope adherence and response generation. In this paper, we attempt to use an LLM for an FCA while constricting the scope of operation and maintaining a guided flow of conversation. We present a strategical combination of discriminative AI methods and generative AI models. Our methodology includes (i) a component of natural language understanding (NLU) operating discriminatively, (ii) a conditional intent-based routing of user messages to appropriate response generators, and (iii) response generators which are either custom ones or open sourced LLMs. The collation of these three strategies realizes a hybrid AI system, assisting FCA with adhering to the defined scope, maintaining context and dialogue flow.</abstract>
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%0 Conference Proceedings
%T Utilizing Large Language Models for Focused Conversational Assistants
%A Dhavalikar, Shruti
%A Vijayan, Karthika
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F dhavalikar-vijayan-2025-utilizing
%X A focused conversational assistant (FCA) realizes human-computer interaction bounded in a predefined scope of operation. With the advent of large language models (LLMs), it has become imperative to integrate them in conversational assistants (CAs). However, an LLM can become largely inaccurate in an FCA with multiple responsibilities, like information extraction, scope adherence and response generation. In this paper, we attempt to use an LLM for an FCA while constricting the scope of operation and maintaining a guided flow of conversation. We present a strategical combination of discriminative AI methods and generative AI models. Our methodology includes (i) a component of natural language understanding (NLU) operating discriminatively, (ii) a conditional intent-based routing of user messages to appropriate response generators, and (iii) response generators which are either custom ones or open sourced LLMs. The collation of these three strategies realizes a hybrid AI system, assisting FCA with adhering to the defined scope, maintaining context and dialogue flow.
%U https://aclanthology.org/2025.ranlp-1.35/
%P 283-290

Markdown (Informal)

[Utilizing Large Language Models for Focused Conversational Assistants](https://aclanthology.org/2025.ranlp-1.35/) (Dhavalikar & Vijayan, RANLP 2025)

• Utilizing Large Language Models for Focused Conversational Assistants (Dhavalikar & Vijayan, RANLP 2025)

ACL

• Shruti Dhavalikar and Karthika Vijayan. 2025. Utilizing Large Language Models for Focused Conversational Assistants. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 283–290, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.