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ML student seeks guidance on human evaluation methods for AI feedback comparison

By PulseAugur Editorial · [1 sources] ·

A master's student is seeking advice on conducting human evaluations for their machine learning project, which involves comparing feedback from a fine-tuned AI model against outputs from Qwen and OpenAI APIs. They are inquiring about the commonality of using surveys for human evaluation in ML research, the adequacy of a 60-participant sample size, strategies for handling inattentive survey respondents, and the practice of conducting follow-up surveys. The student aims to gather feedback to statistically determine which model provides more accurate or useful output. AI

IMPACT This discussion highlights common practices and challenges in evaluating AI model performance through human feedback, relevant for researchers designing experiments.

RANK_REASON The item is a question from a student seeking advice on research methodology, not a primary announcement or finding.

Read on r/MachineLearning →

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

ML student seeks guidance on human evaluation methods for AI feedback comparison

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3 / 100
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The item is a question from a student seeking advice on research methodology, not a primary announcement or finding.
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COVERAGE [1]

  1. r/MachineLearning TIER_1 English(EN) · /u/Dapper-Perspective21 ·

    Is human evaluation through surveys common in ML research?[R][D]

    <!-- SC_OFF --><div class="md"><p>Hi,</p> <p>I am a master's student working on a project where our fine-tuned AI model provides feedback on images. We are also comparing its feedback with outputs from Qwen/OpenAI APIs on the same images.</p> <p>My postdoc and PhD mentors suggest…

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