Cross-Lingual Knowledge Augmentation for Mitigating Generic Overgeneralization in Multilingual Language Models (2025)

Abstract: Generic statements like “birds fly” or “lions have manes” express generalizations about kinds that allow exceptions, yet language models tend to overgeneralize them to universal claims. While previous work showed that ASCENT KB could reduce this effect in English by 30-40%, the effectiveness of broader knowledge sources and the cross-lingual nature of this phenomenon remain unexplored. We investigate generic overgeneralization across English and four South African languages (isiZulu, isiXhosa, Sepedi, SeSotho), comparing the impact of ConceptNet and DBpedia against the previously used ASCENT KB. Our experiments show that ConceptNet reduces overgeneralization by 45-52%% for minority characteristic generics, while DBpedia achieves 48-58%% for majority characteristics, with combined knowledge bases reaching 67%% reduction. These improvements are consistent across all languages, though Nguni languages show higher baseline overgeneralization than Sotho-Tswana languages, potentially suggesting that morphological features may influence this semantic bias. Our findings demonstrate that commonsense and encyclopedic knowledge provide complementary benefits for multilingual semantic understanding, offering insights for developing NLP systems that capture nuanced semantics in low-resource languages.

## introduction generic statements express generalizations about kinds that tolerate exceptions, representing a fundamental aspect of how humans conceptualize and communicate about the world. statements such as "birds fly" or "lions have manes," express truths about these categories despite the fact that penguins cannot fly and female lions lack manes. this 1 https://github.com/sello-ralethe/ multilingual_generics linguistic phenomenon poses a significant challenge for natural language understanding systems, as both humans and language models exhibit a bias toward interpreting these statements as universal claims; a phenomenon known as generic overgeneralization (gog) @xcite . the tendency to overgeneralize from generic statements to universal claims reflects cognitive biases in how humans process categorical information. when presented with a true generic like "ducks lay eggs," people and models tend to incorrectly accept the universal statement "all ducks lay eggs," despite the obvious fact that only female ducks possess this capability @xcite . this effect has been documented in cognitive science literature @xcite and represents an important test case for evaluating whether language models truly understand the nuanced semantics of natural language. recent advances in multilingual representation learning have shown notable success in transferring knowledge across languages, yet the interaction between these methods and language-specific phenomena like genericity remains largely unexplored. this gap is more pronounced for morphologically rich, low-resource languages, where both training data and linguistic resources are scarce @xcite @xcite . languages such as isizulu, isixhosa, sepedi, and sesotho face challenges due to limited digital corpora @xcite . these languages express genericity and other pragmatic phenomena through morphological mechanisms distinct from english, potentially affecting how generic statements are interpreted and overgeneralized. the analytic tools developed for machine translation and representation (e.g. morphology-aware modeling methods) demonstrate that explicit morphological structure affects performance in these contexts @xcite ), yet empirical work on genericity is lacking. in this paper, we present an investigation of generic overgeneralization across multiple languages, examining how this phenomenon manifests in typologically diverse languages and whether knowledge enhancement can mitigate its effects. we make several contributions that advance the understanding of this semantic phenomena. first, we demonstrate that generic overgeneralization is indeed a cross-linguistic phenomenon that affects languages with different morphological systems for expressing genericity. our experiments with four south african languages show patterns in how different language families exhibit this bias, with nguni languages displaying higher baseline overgeneralization than sotho-tswana languages. second, we show that knowledge enhancement through carefully selected knowledge bases can reduce overgeneralization effects. by comparing ascent kb @xcite , conceptnet @xcite , and dbpedia @xcite as knowledge sources, we find that different types of knowledge address different aspects of the overgeneralization problem. conceptnet's commonsense knowledge proves effective for minority characteristic generics, achieving 45-52% relative reduction in overgeneralization, while dbpedia's encyclopedic coverage excels at handling majority characteristic generics with 48-58% reduction. the combination of both knowledge types yields even stronger results, reaching up to 67% reduction in overgeneralization. in this paper, we present the first investigation of generic overgeneralization across morphologically rich, low-resource languages, examining how this phenomenon manifests in typologically diverse settings and whether knowledge enhancement can mitigate its effects across linguistic boundaries. we make several contributions that advance understanding of this semantic phenomenon in multilingual contexts. first, we demonstrate that generic overgeneralization is indeed a cross-linguistic phenomenon, providing empirical evidence across english and four south african languages (isizulu, isixhosa, sepedi, and sesotho) which represent two distinct language families. our experiments reveal systematic patterns in how different language families exhibit this bias, with nguni languages displaying 4-7% higher baseline overgeneralization than sotho-tswana languages, suggesting that morphological features may modulate semantic biases. second, we compare three knowledge sources, demonstrating that different types of knowledge address different aspects of the overgeneralization problem. we show that conceptnet's commonsense knowledge proves effective for minority characteristic generics, achieving 45-52% relative reduction in overgeneralization, while dbpedia's encyclopedic coverage excels at handling majority characteristic generics with 48-58% reduction. the combination of both knowledge types yields even stronger results, reaching up to 67% reduction in overgeneralization. importantly, these improvements remain consistent across all languages, demonstrating that conceptual knowledge effectively transfers across linguistic boundaries despite significant morphological differences. our findings thus offer practical insights for developing nlp systems that capture nuanced semantics in low-resource multilingual settings while advancing theoretical understanding of how semantic biases interact with morphological systems. ## discussion our results provide several insights into generic overgeneralization, the role of knowledge in addressing it, and the cross-lingual nature of this phenomenon. ## conclusion we demonstrate that generic overgeneralization is a universal semantic challenge that manifests across typologically diverse languages, with languagespecific morphological features potentially modulating its expression. our experiments show that combining conceptnet's commonsense knowledge with dbpedia's encyclopedic coverage achieves up to 67% reduction in overgeneralization. our crosslingual analysis uncovers systematic variation between language families, with nguni languages exhibiting 4-7% higher baseline overgeneralization than sotho-tswana languages, possibly due to obligatory plural marking. manual validation of translations shows that morphological errors directly impact generic interpretation, yet knowledge enhancement partially compensates for these artifacts. these findings advance multilingual nlp by demonstrating that conceptual knowledge transfers effectively across languages while highlighting the need for morphology-aware methods in lowresource settings.