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.
Chain-of- M eta W riting: Linguistic and Textual Analysis of How Small Language Models Write Young Students Texts
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
years
2026 8verdicts
UNVERDICTED 8representative citing papers
LLMs generate Xiaohongshu-style posts that elicit social comparison but show stable failures in prompt-based detection of the same reader-grounded signal.
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.
A feature-based decision tree with parsing-derived signals and heuristics detects LLM-generated code in a lightweight, CPU-only setup for SemEval-2026 Task 13.
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
No citing papers match the current filters.