## 2 Related Work
For unmasked sentences (where the LLM predicts the most probable sentence), the main corpora include CrowS-Pairs [36], WinoQueer [37], RedditBias [38], Bias-STS-B [39], PANDA [40], Equity Evaluation Corpus [41], or Bias NLI [42]. For sentence completions (where the LLM provides a continuation to a sentence), the main examples include RealToxicityPrompts [43], BOLD [44], HolisticBias [45], and TrustGPT [46]. A relevant example of a multilingual corpus is HONEST [27], which measures the completion of hurtful sentences in English, Italian, French, Portuguese, Spanish, and Romanian. Despite their usefulness, many of these datasets, especially counterfactual input datasets, face limitations in reliability and validity, with ambiguities about the stereotypes they capture, inconsistencies in the treatment of social groups, and limited generalizability to contexts beyond the US [1; 8; 47]. HESEIA is a large-scale dataset co-designed in Latin American school settings, capturing intersectional biases across multiple demographic axes and school subjects, reflecting local contexts [18]. TWC (Translate-with-Care) has been created to assess gender bias and logical consistency in machine translation, especially between naturally gendered and non-gendered languages [24]. EuroGEST is an expansion of the GEST dataset to 30 European languages for a more holistic assessment of how gender biases are embedded in multilingual models [15]. In recent years, the development of annotated Spanish-language datasets aiming at the identification of toxicity, hate speech, and aggression has expanded considerably. Ref. [11] provides a systematic categorization of existing resources, highlighting their main features, annotation schemes, and limitations. Table 1 synthesizes this overview, positioning the newly developed _EsCorpiusBias_ corpus within this evolving landscape. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  
Initial datasets such as AMI-2018 [48] and MEX-A3T [49; 50] primarily leveraged Twitter data, focusing explicitly on misogyny and aggressiveness via binary or simplified annotation schemes. These datasets typically lacked substantial discourse context despite having significant proportions of toxic content. Subsequent efforts such as HateEval-2019 [51] and HateNet-2019 [52] further targeted hate speech on Twitter, extending their scope to xenophobia and immigration-related hate. More recent initiatives, including EXIST 2021/2022 and OffendES/OffendMEX [53; 54; 55], adopted richer classification frameworks to identify various forms of offensiveness across platforms such as YouTube and Instagram. Notably, recent corpora like contextualized hate speech, NewsCom-TOX, and DETESTS-Dis [11; 12; 13; 56] explicitly incorporate context by annotating news comments and social media posts, evaluating stereotypes, stance, sarcasm, implicitness, and insults. The availability of these annotated datasets has directly facilitated advances in machine learning approaches to detect toxicity in the Spanish language. Recent research extensively applies transformer-based models fine-tuned on these datasets, showing notable improvements over traditional approaches. Table 2 summarizes representative deep learning models, highlighting their base architectures, target phenomena, and distinguishing methodological aspects. BETO-based [57] models (e.g., [12; 55]) tend to outperform multilingual transformers (e.g., [58; 59]), highlighting the importance of context-aware embeddings and domain-specific fine-tuning to effectively capture linguistic subtleties in Spanish. Despite this progress, research on toxicity detection in Spanish still suffers from several key deficiencies. First, there is a lack of consistency in the definitions and taxonomies used for toxicity-related phenomena, which presents challenges in comparing models and evaluation benchmarks. Second, the predominance of Twitter-based datasets continues to limit the generalizability of models to other discourse genres such as forums, long-form discussions, and multimodal contexts.