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.
HyperCLOVA X technical report.Preprint at https://arxiv.org/abs/2404.01954(2024)
7 Pith papers cite this work. Polarity classification is still indexing.
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LLM gender stereotyping across four languages spans roughly 2.5 times the human cross-country range on HEXACO-100, with translation altering specific stereotyped attributes and effects that can compound.
A framework extracts embeddings from Korean-English bilingual LLMs across thousands of spaces and uses similarity distributions plus logistic classifiers to identify lexical gaps with AUCs of 0.81 and 0.76.
K-MetBench shows LLMs have large gaps in interpreting meteorology diagrams and Korean-specific context, with smaller local models beating much larger global ones.
SCRIPT is a model-agnostic injection module that enhances Korean PLM embeddings with subcharacter compositional knowledge from Jamo, leading to better performance on NLU and NLG tasks and more linguistically coherent embedding spaces.
Selective-pivot-token training plus layer-averaging-with-recurrence reportedly gives 2.5x parameter compression on a small Korean LLM, but the efficiency claim lacks its decisive controls and the abstract advertises results absent from the body.
SemEval-2026 Task 7 presents a benchmark and two evaluation tracks for assessing LLMs on everyday knowledge in diverse languages and cultures without allowing training on the test data.
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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Anchoring LLM Gender Bias to Human Baselines: A Cross-Lingual Audit
LLM gender stereotyping across four languages spans roughly 2.5 times the human cross-country range on HEXACO-100, with translation altering specific stereotyped attributes and effects that can compound.
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Discovering Lexical Gaps Using Embeddings from Multilingual LLMs
A framework extracts embeddings from Korean-English bilingual LLMs across thousands of spaces and uses similarity distributions plus logistic classifiers to identify lexical gaps with AUCs of 0.81 and 0.76.
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K-MetBench: A Multi-Dimensional Benchmark for Fine-Grained Evaluation of Expert Reasoning, Locality, and Multimodality in Meteorology
K-MetBench shows LLMs have large gaps in interpreting meteorology diagrams and Korean-specific context, with smaller local models beating much larger global ones.
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SCRIPT: A Subcharacter Compositional Representation Injection Module for Korean Pre-Trained Language Models
SCRIPT is a model-agnostic injection module that enhances Korean PLM embeddings with subcharacter compositional knowledge from Jamo, leading to better performance on NLU and NLG tasks and more linguistically coherent embedding spaces.
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CHERRY: Compressed Hierarchical Experts with Recurrent Representational Yield
Selective-pivot-token training plus layer-averaging-with-recurrence reportedly gives 2.5x parameter compression on a small Korean LLM, but the efficiency claim lacks its decisive controls and the abstract advertises results absent from the body.
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SemEval-2026 Task 7: Everyday Knowledge Across Diverse Languages and Cultures
SemEval-2026 Task 7 presents a benchmark and two evaluation tracks for assessing LLMs on everyday knowledge in diverse languages and cultures without allowing training on the test data.