4arXiv cs.CL (Computation and Language)·
ToxGate: Conditional trust-fusion for multilingual and code-mixed abuse detection
A new arXiv preprint introduces ToxGate, a trust-fusion head that conditions external toxicity signals (English toxicity scores, Indic abuse priors, rule-based severity cues) on encoder representations before incorporating them into abuse detection predictions. The method is evaluated across three short-text abuse datasets, four transformer encoders, and five seeds, improving over baseline encoders in 10 of 12 in-domain and 7 of 8 transfer settings. The core finding is that external toxicity tools should be treated as conditional evidence rather than fixed features, with the largest gains in high-risk slices such as explicit slurs, violent threats, and cross-dataset transfer. The work targets a practical gap in content moderation for Indian multilingual and code-mixed text.
Related guides (2)
Related events (8)
ToxiREX: Multilingual contextual dataset for implicit toxicity detection with structured reasoning schema
Researchers introduce ToxiREX, a multilingual Reddit-based dataset for detecting implicit and context-dependent toxicity across six languages (English, Arabic, Turkish, Spanish, German, Dutch), anchored to real-world events like the 2023 Turkey earthquakes and the Russian invasion of Ukraine. The dataset includes 125K LLM-annotated training comments and ~3K human-annotated test comments, structured using a toxic reasoning schema that captures implicit toxicity and maps to existing taxonomies. Baseline results from prompted and fine-tuned language models show above-random but substantially suboptimal performance, indicating the task remains challenging. ToxiREX is claimed as the first dataset combining multilingual coverage, conversational context, and implicit toxicity with schema-based structured annotations.
TRACE: Lightweight RAG corpus poisoning detection via token influence attribution
Researchers introduce TRACE, a detection framework for corpus poisoning attacks on Retrieval-Augmented Generation (RAG) systems that works by tracing answer-related tokens through token influence attribution rather than relying on auxiliary classifiers or LLM-based verification. The method identifies recurrent high-influence keywords across retrieved documents and performs secondary verification to confirm their effect on model predictions. Evaluated on three QA benchmarks and six LLMs, TRACE achieves strong detection performance while also exposing attacker-specified target answers, with lower computational overhead than prior approaches.
Unified defense framework detects and remediates data poisoning in text summarization fine-tuning
A new arXiv preprint introduces a post-hoc defense framework for detecting and recovering from training-time data poisoning in LLMs fine-tuned for abstractive summarization. The framework uses influence-function analysis in white-box settings and behavioral perturbation auditing in black-box settings, achieving 85-92% detection precision across nine architectures and six benchmarks. Gradient-ascent unlearning restores up to 96% of original model behavior with less than 0.6% ROUGE degradation. The authors also introduce novel attacks targeting factual distortion and representational bias that evade conventional evaluation metrics.
Provenance-grounded gating and adaptive recovery improve synthetic post-training data curation
A controlled study examines two underexplored practices in synthetic post-training data pipelines: grounding filtering signals in source provenance and systematically recovering rejected samples rather than discarding them. Using adversarially injected corpora as ground-truth failure labels, the authors find that exact source provenance improves faithfulness gating for stronger judges, that hallucination and reward gates reject largely disjoint populations (making both necessary), and that adaptive recovery via failure diagnosis and targeted regeneration outperforms naive resampling. Generator scale is the primary driver of downstream fine-tuning quality, with filtration and recovery contributing meaningfully but secondarily.
Tatoxa: State-of-the-art text detoxification system for the low-resource Tatar language
Researchers introduce Tatoxa, a text detoxification system for the Tatar language, along with a new fine-tuning and evaluation dataset for this low-resource setting. Comparative experiments show Tatoxa outperforms both open-source and proprietary LLMs on quality metrics. Cross-lingual transfer experiments find that even culturally close Russian data transfers poorly compared to native Tatar training data, highlighting the limits of cross-lingual approaches for low-resource languages.
Multi-stage explainability framework translates transformer speech models into clinical cognitive impairment narratives
A new arXiv preprint proposes a framework for making transformer-based speech cognitive impairment detection clinically interpretable by combining SHAP token attribution, linguistic feature analysis, and a four-stage LLM reasoning pipeline using LLaMA-3.1-70B-Instruct. The system is built on the SpeechCARE-Adaptive Gating Network multimodal model (F1=72.11% on NIA PREPARE) and maps outputs to four cognitive-linguistic dimensions. Physician evaluation on 70 samples showed strong alignment with clinical profiles and a System Usability Scale score of 82/100, suggesting practical clinical workflow integration potential.
Domain-specific transformer embeddings for detecting dosing errors in clinical trial protocols
Researchers from CaresAI evaluated biomedical transformer models (ClinicalBERT, PubMedBERT, BioBERT, MedCPT) for detecting dosing errors in clinical trial protocols, combining text embeddings with structured metadata and classical ML classifiers. BioBERT achieved the best single-encoder performance at ROC-AUC 0.794, while gradient boosting and SVM ensembles reached 0.821–0.853. The study finds that domain alignment of the encoder matters more than stacking multiple embeddings, and demonstrates a practical NLP pipeline for clinical trial safety monitoring.
RDS Fusion: Hybrid neuro-symbolic gating with compressed CoT for zero-shot irony detection
Researchers introduce the Robust Dual-Signal (RDS) Fusion framework, a hybrid neuro-symbolic architecture that compresses Chain-of-Thought reasoning without supervised fine-tuning for irony and sarcasm detection in social media text. Evaluated on TweetEval (N=734) and iSarcasm, the zero-shot system matches fine-tuned BERTweet performance and outperforms supervised SemEval transformer ensembles on the imbalanced iSarcasm dataset. A statistical ablation shows that only the full concurrent fusion of all three signals yields a validated improvement, with individual components providing no significant standalone gain.