Ko-WideSearch is a new Korean breadth-search benchmark spanning 16 categories and three difficulty tiers that evaluates web agents on full set membership plus per-item attributes, showing consistent gaps between set recovery and row completion.
arXiv preprint arXiv:2105.09680 , url=
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Steering vectors from frozen LM layers enable a lightweight classifier to detect machine-generated text robustly across domains, source models, and editing attacks.
An image-semantic guided method enhances MLLMs for detecting AI-generated modern Chinese poetry by combining poem text with visual representations of content, achieving 85.65% Macro-F1 with Gemini and outperforming text baselines and RoBERTa.
A topic-modeling framework measures document-level thematic consistency in translations by aligning key tokens across languages with a bilingual dictionary and scoring via cosine similarity, providing explainable insights beyond sentence-level metrics.
KG2Cypher generates validated synthetic Text-Cypher pairs from existing KGs, trains LoRA models with class-conditioned schema prompting, and reports execution-result F1 gains to 0.950 and 0.92 plus 95.2% exact match in Korean enterprise settings.
citing papers explorer
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Ko-WideSearch: A Korean Breadth-Search Benchmark for Exhaustive Set Enumeration by Web Agents
Ko-WideSearch is a new Korean breadth-search benchmark spanning 16 categories and three difficulty tiers that evaluates web agents on full set membership plus per-item attributes, showing consistent gaps between set recovery and row completion.
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SV-Detect: AI-generated Text Detection with Steering Vectors
Steering vectors from frozen LM layers enable a lightweight classifier to detect machine-generated text robustly across domains, source models, and editing attacks.
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Seeing the Poem: Image-Semantic Detection of AI-Generated Modern Chinese Poetry with MLLMs
An image-semantic guided method enhances MLLMs for detecting AI-generated modern Chinese poetry by combining poem text with visual representations of content, achieving 85.65% Macro-F1 with Gemini and outperforming text baselines and RoBERTa.
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An Explainable Approach to Document-level Translation Evaluation with Topic Modeling
A topic-modeling framework measures document-level thematic consistency in translations by aligning key tokens across languages with a bilingual dictionary and scoring via cosine similarity, providing explainable insights beyond sentence-level metrics.
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KG2Cypher: Data-Centric Pipeline for Building Enterprise Text-to-Cypher Systems
KG2Cypher generates validated synthetic Text-Cypher pairs from existing KGs, trains LoRA models with class-conditioned schema prompting, and reports execution-result F1 gains to 0.950 and 0.92 plus 95.2% exact match in Korean enterprise settings.