mlsanityData to 14 Oct 2026

ToxiREX: A Dataset on Toxic REasoning in ConteXt

arXiv 2606.27981 ↗First posted 26 Jun 2026cs.CLcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

We introduce a new, contextual, multilingual dataset called ToxiREX: Toxic REasoning in ConteXt. The dataset consists of threads of Reddit comments and structured characterizations of what the comments imply, following a systematic toxic reasoning schema developed in a previous paper. Using the schema allows us to capture and explain implicit and context-dependent toxicity, while supporting mappings to existing toxicity taxonomies. The dataset includes comments in six languages (English, Arabic, Turkish, Spanish, German, and Dutch), collected from posts connected to specific major events (e.g. the 2023 Turkey earthquakes; the Russian invasion of Ukraine). We describe the context-preserving preprocessing of the threads. We create a training set of 125 thousand comments which is annotated by a commercially available LLM, and a test set of just under three thousand comments that is annotated by native speakers. We show that apparent disagreements in the test set annotations often reflect defensible alternative interpretations rather than noise. Finally, we provide baseline results by prompting and fine-tuning language models. To produce these results, we develop evaluation strategies for our hierarchical, schema-based predictions. While models perform better than random, there remains a lot of room for improvement, showing the task to be challenging. ToxiREX is the first dataset to simultaneously incorporate multiple languages, conversational context, and implicit toxicity, while using the toxic reasoning schema for rich, structured annotations. Dataset available at: https://github.com/cltl/toxirex

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 19 of its 53 references that are in the map. First posted on arXiv 26 Jun 2026.

Builds on

19 of 53 references.

References · 19

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  3. 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,386
  4. 2020HateXplain: A Benchmark Dataset for Explainable Hate Speech Detectioncited by 407
  5. 2019Social Bias Frames: Reasoning about Social and Power Implications of Languagecited by 380
  6. 2021Latent Hatred: A Benchmark for Understanding Implicit Hate Speechcited by 217
  7. 2023Efficient Guided Generation for Large Language Modelscited by 200
  8. 2020Directions in Abusive Language Training Data: Garbage In, Garbage Outcited by 140
  9. 2019Multilingual and Multi-Aspect Hate Speech Analysiscited by 133
  10. 2020Toxicity Detection: Does Context Really Matter?cited by 93
  11. 2022Handling Bias in Toxic Speech Detection: A Surveycited by 61
  12. 2023SemEval-2023 Task 10: Explainable Detection of Online Sexismcited by 48
7 more
  1. 2022Hate Speech and Counter Speech Detection: Conversational Context Does Mattercited by 32
  2. 2025EuroBERT: Scaling Multilingual Encoders for European Languagescited by 31
  3. 2021A systematic review of Hate Speech automatic detection using Natural Language Processingcited by 27
  4. 2022Explainable Abuse Detection as Intent Classification and Slot Fillingcited by 11
  5. 2024Target Span Detection for Implicit Harmful Contentcited by 8
  6. 2023LAHM : Large Annotated Dataset for Multi-Domain and Multilingual Hate Speech Identificationcited by 2
  7. 2024Grounding Toxicity in Real-World Events across Languagescited by 1

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  1. 2025ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection
  2. 2021Toxicity Detection can be Sensitive to the Conversational Context
  3. 2025Something Just Like TRuST : Toxicity Recognition of Span and Target
  4. 2024RTP-LX: Can LLMs Evaluate Toxicity in Multilingual Scenarios?
  5. 2025Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective
  6. 2025SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models
  7. 2025Towards Safer Pretraining: Analyzing and Filtering Harmful Content in Webscale datasets for Responsible LLMs
  8. 2025<think> So let's replace this phrase with insult... </think> Lessons learned from generation of toxic texts with LLMs