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LLMs outperform traditional ML in survey analysis, but raise explainability concerns

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper compares the performance of large language models (LLMs) like OpenAI's GPT series and Meta's LLaMA against traditional machine learning models for analyzing open-ended survey responses. The study found that LLMs generally outperform traditional methods in classification accuracy, particularly in identifying complex sentiment and thematic patterns. However, the research also highlights significant differences in how LLMs justify their predictions and maintain consistency, presenting trade-offs between predictive power and explainability for qualitative research. AI

IMPACT LLMs offer improved accuracy for qualitative research but introduce challenges in consistency and explainability.

RANK_REASON The cluster contains an academic paper published on arXiv comparing LLMs to traditional ML models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs outperform traditional ML in survey analysis, but raise explainability concerns

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The cluster contains an academic paper published on arXiv comparing LLMs to traditional ML models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Abdullah Akinde, Mariam Akinde, Rasheedat Emiola, Ahmed Akinsola ·

    So Many Opinions, So Many LLMs: Comparing Large Language Models to Traditional Machine Learning for Open- Ended Survey Analysis

    arXiv:2607.11890v1 Announce Type: cross Abstract: Open-ended surveys offer valuable insights, but they are notoriously difficult to analyze at scale. Building on previous work that employed traditional machine learning to classify text ("So Many Responses, So Little Time: A Machi…

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