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Large Language Models (LLMs) are crucial for qualitative analysis because they offer automation and interpretive information. Also, the computation time for LLM is much shorter than that of software-assisted manual qualitative analysis. However, LLM hallucinations can lead to misleading or incorrect outputs that pose a significant challenge to reliability and accuracy. This study identified and examined the root causes of 12 types of hallucinations in LLM-based qualitative analysis. To mitigate these hallucinations, a systematic system prompts refinement, spurious noise filtering, and controlled batch processing of transcripts were adopted to optimize and enhance the reliability and precision of LLM-based qualitative research results.

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Performance Evaluation of LLM Hallucination Reduction Strategies for Reliable Qualitative Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Aisvarya Adeseye,
  • Jouni Isoaho,
  • Mohammad Tahir

摘要

Large Language Models (LLMs) are crucial for qualitative analysis because they offer automation and interpretive information. Also, the computation time for LLM is much shorter than that of software-assisted manual qualitative analysis. However, LLM hallucinations can lead to misleading or incorrect outputs that pose a significant challenge to reliability and accuracy. This study identified and examined the root causes of 12 types of hallucinations in LLM-based qualitative analysis. To mitigate these hallucinations, a systematic system prompts refinement, spurious noise filtering, and controlled batch processing of transcripts were adopted to optimize and enhance the reliability and precision of LLM-based qualitative research results.