A matched benchmark shows GUI computer-use agents at 59.1% full pass rate versus 48.2% for original-skill CLI agents, rising to 69.3% with verifier-guided augmentation, indicating modality-specific execution bottlenecks.
Building Safe G en AI Applications: An End-to-End Overview of Red Teaming for Large Language Models
6 Pith papers cite this work. Polarity classification is still indexing.
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Introduces LCAM, a five-layer framework distinguishing underfit and overreach misalignments to evaluate conversational AI on perceptual, semantic, affective, cognitive, and ethical fit.
LLM-generated ML pipelines show higher bias (87.7% sensitive attributes) than conditional statements (59.2%), indicating that simple if-statement tests underestimate bias risk in practical code generation.
Finetuning Qwen3-32B with data augmentation and self-training achieves competitive 8th-place ranking on SemEval-2026 conspiracy detection.
Finetuning LLMs with QLoRA and multilingual data augmentation for polarization detection, type, and manifestation in SemEval-2026 Task 9.
Fine-tuning LLMs by adapting the mdok approach produces competitive results on binary detection, source attribution, and hybrid/adversarial code identification in SemEval-2026 Task 13.
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LCAM: A Framework for Diagnosing Interactional Alignment Failures in Con-versational AI
Introduces LCAM, a five-layer framework distinguishing underfit and overreach misalignments to evaluate conversational AI on perceptual, semantic, affective, cognitive, and ethical fit.