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This study uses Large Language Models (LLMs) for extracting intents within the field of career guidance. Collating discussions from a popular social media platform, BERTopic, a state-of-the-art topic modeling technique leveraging Bidirectional Encoder Representations from Transformers (BERT) embeddings is employed to extract key career-related themes. Our analysis evaluates BERTopic's proficiency, particularly its integration with LLMs, to refine topic modeling and automate the intent extraction process. A central focus lies on the application of Generative AI for automatic topic labeling, contrasting the performance of proprietary models like OpenAI's GPT-3.5 with open-source models such as Llama-2. Subsequently, the study uses these mined intents to fine-tune a BERT based LLM, scrutinizing its efficacy in intent classification against a Random Forest baseline model. The BERT model demonstrates a remarkable improvement in multi-classification accuracy of 0.92. Our results underscore the profound and emerging capabilities of LLMs to integrate in task based chatbots that can offer nuanced, tailored career guidance, heralding potentially a new era of AI-enabled educational support. To ensure the reproducibility of our results and foster further research, the dataset and code is publicly made available.

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Enhancing Career Guidance Through Intent Mining with Large Language Models

  • 会议论文

  • 发表日期：

    2025年

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  • Mohit Bhatnagar

摘要

This study uses Large Language Models (LLMs) for extracting intents within the field of career guidance. Collating discussions from a popular social media platform, BERTopic, a state-of-the-art topic modeling technique leveraging Bidirectional Encoder Representations from Transformers (BERT) embeddings is employed to extract key career-related themes. Our analysis evaluates BERTopic's proficiency, particularly its integration with LLMs, to refine topic modeling and automate the intent extraction process. A central focus lies on the application of Generative AI for automatic topic labeling, contrasting the performance of proprietary models like OpenAI's GPT-3.5 with open-source models such as Llama-2. Subsequently, the study uses these mined intents to fine-tune a BERT based LLM, scrutinizing its efficacy in intent classification against a Random Forest baseline model. The BERT model demonstrates a remarkable improvement in multi-classification accuracy of 0.92. Our results underscore the profound and emerging capabilities of LLMs to integrate in task based chatbots that can offer nuanced, tailored career guidance, heralding potentially a new era of AI-enabled educational support. To ensure the reproducibility of our results and foster further research, the dataset and code is publicly made available.