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If a language model can recognize code it wrote, it may favor that code as a judge, and instances of one model monitoring each other could collude, and instances of one model monitoring each other could collude.
This work presents the first systematic characterization of language-of-study (LoS) bias, distinguishing negative and positive forms, and introduces the human-annotated dataset LOBSTER, which identifies four subcategories of negative bias, and finds that demanding unjustified cross-lingual generalization is the most do...
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