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Misuse of 'curve overfitting' term in LLM context highlighted

By PulseAugur Editorial · [1 sources] ·

The term "curve overfitting" is being misused when applied to large language models that reproduce verbatim fragments of their training data. This specific behavior is not true overfitting, which refers to models performing well on training data but poorly on new, unseen samples. The misuse of the term aims to deceive individuals unfamiliar with statistical algorithms and neural networks. AI

IMPACT Clarifies technical terminology, preventing misinformation about LLM capabilities and limitations.

RANK_REASON The item discusses the misuse of a technical term in the context of LLMs, which falls under commentary.

Read on Mastodon — fosstodon.org →

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

Misuse of 'curve overfitting' term in LLM context highlighted

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Commentary
The item discusses the misuse of a technical term in the context of LLMs, which falls under commentary.
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opinion
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High
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73 days old
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COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    😀 I know very well (and faced several times) curve overfitting: it's a term related to models working very well on the "training data", but badly on new samples

    😀 I know very well (and faced several times) curve overfitting: it's a term related to models working very well on the "training data", but badly on new samples outside that set. Using the term for LLM output reproducing verbatim fragments of the training data is just mischaracte…

    LINKS en.wikipedia.org/…/Lossy_compression

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