Identifying linguistic bias in text demands the identification not only of explicitly asserted content but also of implicit content including presuppositions. Large language models (LLMs) offer a promising automated approach to detecting presuppositions, yet the extent to which their judgments align with human intuitions remains unexplored. Moreover, LLMs may inadvertently reflect societal biases when identifying presupposed content. To empirically investigate this, we prompt multiple large language models to evaluate presuppositions across diverse textual domains, drawing from three distinct datasets annotated by human raters. We calculate the agreement between LLMs and human raters, and find several linguistic factors associated with fluctuations in human-model agreement. Our observations reveal discrepancies in human-model alignment, suggesting potential biases in LLMs, notably influenced by gender and political ideology.

We present a definiteness annotation scheme that captures the semantic, pragmatic, and discourse information, which we call communicative functions, associated with linguistic descriptions such as “a story about my speech”, “the story”, “every time I give it”, “this slideshow”. A survey of the literature suggests that definiteness does not express a single communicative function but is a grammaticalization of many such functions, for example, identifiability, familiarity, uniqueness, specificity. Our annotation scheme unifies ideas from previous research on definiteness while attempting to remove redundancy and make it easily annotatable. This annotation scheme encodes the communicative functions of definiteness rather than the grammatical forms of definiteness. We assume that the communicative functions are largely maintained across languages while the grammaticalization of this information may vary. One of the final goals is to use our semantically annotated corpora to discover how definiteness is grammaticalized in different languages. We release our annotated corpora for English and Hindi, and sample annotations for Hebrew and Russian, together with an annotation manual.