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Compared to the monolingual setup, we see much less work on code-mixed hate as large-scale annotated hate corpora are unavailable for the study. To overcome this bottleneck, we propose using native language hate samples (native language samples\/ native samples hereafter). We hypothesise that in the era of multilingual language models (MLMs), hate in code-mixed settings can be detected by majorly relying on the native language samples. Even though the NLP literature reports the effectiveness of MLMs on hate detection in many cross-lingual settings, their extensive evaluation in a code-mixed scenario is yet to be done. This article attempts to fill this gap through rigorous empirical experiments. We considered the Hindi-English code-mixed setup as a case study as we have the linguistic expertise for the same. Some of the interesting observations we got are: (i) adding native hate samples in the code-mixed training set, even in small quantity, improved the performance of MLMs for code-mixed hate detection, (ii) MLMs trained with native samples alone observed to be detecting code-mixed hate to a large extent, (iii) the visualisation of attention scores revealed that, when native samples were included in training, MLMs could better focus on the hate emitting words in the code-mixed context, and (iv) finally, when hate is subjective or sarcastic, naively mixing native samples doesn\u2019t help much to detect code-mixed hate. We have released the data and code repository to reproduce the reported results.\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>1<\/jats:sup>\n            <\/jats:xref>\n          <\/jats:p>","DOI":"10.1145\/3726866","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T09:53:54Z","timestamp":1743155634000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving Code-Mixed Hate Detection by Native Sample Mixing: A Case Study for Hindi-English Code-Mixed Scenario"],"prefix":"10.1145","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2389-9204","authenticated-orcid":false,"given":"Debajyoti","family":"Mazumder","sequence":"first","affiliation":[{"name":"DSE, IISER, Bhopal, Bhopal, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9735-1507","authenticated-orcid":false,"given":"Aakash","family":"Kumar","sequence":"additional","affiliation":[{"name":"DSE, IISER, Bhopal, Bhopal, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2461-9679","authenticated-orcid":false,"given":"Jasabanta","family":"Patro","sequence":"additional","affiliation":[{"name":"DSE, IISER, Bhopal, Bhopal, India"}]}],"member":"320","published-online":{"date-parts":[[2025,4,24]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"10","volume-title":"Proceedings of the 6th International Conference on Computer Science and Information Technology","volume":"10","author":"Al-Hassan Areej","year":"2019","unstructured":"Areej Al-Hassan and Hmood Al-Dossari. 2019. 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