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.
WETB ench: A Benchmark for Detecting Task-Specific Machine-Generated Text on W ikipedia
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
years
2026 10verdicts
UNVERDICTED 10representative citing papers
An adversarial methodology generates a multilingual cross-platform dataset of paired human-AI social messages, and models trained on it outperform prior detectors on real-world out-of-distribution data.
Reward models for LLMs frequently select socially undesirable options across four social domains, show no overall best performer, and exhibit a bias-avoidance versus context-sensitivity trade-off.
LLMs generate Xiaohongshu-style posts that elicit social comparison but show stable failures in prompt-based detection of the same reader-grounded signal.
Lexical richness is a robust linguistic signal for AI-generated text detection across models and domains, while most other features are context-dependent.
Cross-lingual transfer and language-specific data efforts are interdependent and complementary for effective low-resource NLP, as demonstrated through Luxembourgish case studies and synthesis.
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.
citing papers explorer
-
Adversarial Creation and Detection of AI-Generated Social Bot Content
An adversarial methodology generates a multilingual cross-platform dataset of paired human-AI social messages, and models trained on it outperform prior detectors on real-world out-of-distribution data.
-
Misaligned by Reward: Socially Undesirable Preferences in LLMs
Reward models for LLMs frequently select socially undesirable options across four social domains, show no overall best performer, and exhibit a bias-avoidance versus context-sensitivity trade-off.
-
Psychologically Potent, Computationally Invisible: LLMs Generate Social-Comparison-Eliciting Posts They Fail to Detect
LLMs generate Xiaohongshu-style posts that elicit social comparison but show stable failures in prompt-based detection of the same reader-grounded signal.
-
A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models
Lexical richness is a robust linguistic signal for AI-generated text detection across models and domains, while most other features are context-dependent.
-
Why Low-Resource NLP Needs More Than Cross-Lingual Transfer: Lessons Learned from Luxembourgish
Cross-lingual transfer and language-specific data efforts are interdependent and complementary for effective low-resource NLP, as demonstrated through Luxembourgish case studies and synthesis.
-
From If-Statements to ML Pipelines: Revisiting Bias in Code-Generation
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.
-
mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection
Finetuning Qwen3-32B with data augmentation and self-training achieves competitive 8th-place ranking on SemEval-2026 conspiracy detection.
-
mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection
Finetuning LLMs with QLoRA and multilingual data augmentation for polarization detection, type, and manifestation in SemEval-2026 Task 9.