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Local LLM struggles with consistent text labeling despite high accuracy

By PulseAugur Editorial · [2 sources] ·

A recent experiment explored how effectively local Large Language Models (LLMs) can label text, specifically using the Qwen2.5 3B Instruct model. When tasked with classifying 312 customer reviews into "praise," "complaint," or "question," the model achieved high accuracy, correctly labeling 311 out of 312 reviews when responses were normalized to lowercase. However, the model exhibited variability in its output, producing six different variations of the labels in English and an unclassifiable "不溝" in Japanese due to tokenization issues. This highlights a challenge in ensuring consistent, predictable output from LLMs even when they understand the core task. AI

IMPACT Demonstrates challenges in achieving consistent LLM output for classification tasks, even with high accuracy, due to tokenization and output variability.

RANK_REASON The cluster details an experiment and analysis of LLM performance on a specific task, including code and methodology.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Local LLM struggles with consistent text labeling despite high accuracy

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44 / 100
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Research
The cluster details an experiment and analysis of LLM performance on a specific task, including code and methodology.
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2 independent sources
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model release, product
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High
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Breaking (< 6h)
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COVERAGE [2]

  1. dev.to — LLM tag TIER_1 Español(ES) · Hanako H. ·

    How to label text with a local LLM to only respond with your labels

    <p>Etiquetar filas con un modelo de lenguaje parece la tarea más fácil que se le puede dar. Envías cada fila con una instrucción del tipo "queja, elogio o pregunta, solo la etiqueta", guardas lo que vuelve y cuentas. La acción Run Model de la pantalla Datasets de la aplicación de…

  2. dev.to — LLM tag TIER_1 English(EN) · Hanako H. ·

    How to label text with a local LLM so it only answers with your labels

    <p>Labelling rows with a language model looks like the easiest job you can give it. Send each row with an instruction such as "complaint, praise or question, only the label", keep what comes back, count. The Run Model action in the Datasets screen of the <a href="https://osfoundr…

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