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@@ -52,12 +52,12 @@ artificial intelligence is incapable of.**
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## Model Bias and Language Quality
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The underlying model's training biases surface within this pipeline.
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Output quality tracks with how well a language is represented in the training
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corpus: standard French (`fr-FR`) produces coherent text; regional variants like
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`fr-CD` and `fr-CI` are noticeably weaker; low-resource languages like Welsh,
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Māori, and Sicilian produce output that is syntactically plausible but often
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semantically broken.
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The underlying model's training biases surface within this pipeline. Output
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quality tracks with how well a language is represented in the training corpus:
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standard French (`fr-FR`) produces coherent text; regional variants like `fr-CD`
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and `fr-CI` are noticeably weaker; low-resource languages like Welsh, Māori, and
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Sicilian produce output that is syntactically plausible but often semantically
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broken.
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This is a property of the training distribution, not something that can be
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mitigated through prompt design. This is a well-documented characteristic of
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@@ -235,7 +235,9 @@ idiomatic phrasing.
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]
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```
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This dataset, when fed into the pipeline will often times reason that a local variant of French is needed, but will often times just default to a standardized dialect of French, devoid of any cultural or linguistic nuance.
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This dataset, when fed into the pipeline will often times reason that a local
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variant of French is needed, but will often times just default to a standardized
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dialect of French, devoid of any cultural or linguistic nuance.
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For languages such as Welsh (Wales), Māori (Aotearoa/New Zealand), or Sicilian
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(Sicily, Italy), the model can generate text that looks syntactically plausible
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@@ -260,7 +262,13 @@ Output sample:
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## Footnotes
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[^llm-choke]: CHOKE (Certain Hallucinations Overriding Known Evidence) is a hallucination failure mode defined by Simhi et al. (2025), in which a model that can consistently answer a question correctly produces a confident, wrong response when the prompt is trivially perturbed. Source: Trust Me, I'm Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer — Adi Simhi, Itay Itzhak, Fazl Barez, Gabriel Stanovsky, Yonatan Belinkov.
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[^llm-choke]:
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CHOKE (Certain Hallucinations Overriding Known Evidence) is a hallucination
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failure mode defined by Simhi et al. (2025), in which a model that can
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consistently answer a question correctly produces a confident, wrong
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response when the prompt is trivially perturbed. Source: Trust Me, I'm
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Wrong: LLMs Hallucinate with Certainty Despite Knowing the Answer — Adi
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Simhi, Itay Itzhak, Fazl Barez, Gabriel Stanovsky, Yonatan Belinkov.
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[^llm-bias]:
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e.g., Blasi et al. (2022), "Systematic Inequalities in Language Technology
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@@ -279,8 +287,12 @@ Output sample:
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[creativecommons.org/licenses/by-sa/4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.en).
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[^diacetyl-source]:
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White Labs confirms that diacetyl is a yeast-derived fermentation byproduct: specifically, a compound produced during amino acid metabolism that leaks out of the yeast cell and oxidises into its characteristic buttery off-flavour. It is generally considered undesirable at any perceived level in most styles, though low levels are tolerated in some English ales and European lagers.
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Source:
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White Labs confirms that diacetyl is a yeast-derived fermentation byproduct:
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specifically, a compound produced during amino acid metabolism that leaks
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out of the yeast cell and oxidises into its characteristic buttery
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off-flavour. It is generally considered undesirable at any perceived level
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in most styles, though low levels are tolerated in some English ales and
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European lagers. Source:
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[whitelabs.com — Compound Spotlight: Diacetyl](https://www.whitelabs.com/news-update-detail?id=54).
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[^diacetyl-rest]:
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