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.
M onte C arlo Temperature: a robust sampling strategy for LLM ' s uncertainty quantification methods
6 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
6
Pith papers citing it
3
external citations · OpenAlex
years
2026 6representative citing papers
A large-scale multilingual evaluation of LLM uncertainty estimation methods across 22 languages and 9 models finds that English reasoning closes the UE gap for low-resource languages and that optimal UE method choice depends on model scale.
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
-
GUI vs. CLI: Execution Bottlenecks in Screen-Only and Skill-Mediated Computer-Use Agents
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.
-
Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs
A large-scale multilingual evaluation of LLM uncertainty estimation methods across 22 languages and 9 models finds that English reasoning closes the UE gap for low-resource languages and that optimal UE method choice depends on model scale.
-
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.
-
mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code
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.