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20242026
most citedHyperCLOVA X Technical Report

7 citations · 11 across the 11 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CL2024

Code-Switching Curriculum Learning for Multilingual Transfer in LLMs

Haneul Yoo, Cheonbok Park, Sangdoo Yun +2

Large language models (LLMs) now exhibit near human-level performance in various tasks, but their performance drops drastically after a handful of high-resource languages due to th…

cs.CL2024

Mitigating Semantic Leakage in Cross-lingual Embeddings via Orthogonality Constraint

Dayeon Ki, Cheonbok Park, Hyunjoong Kim

Accurately aligning contextual representations in cross-lingual sentence embeddings is key for effective parallel data mining. A common strategy for achieving this alignment involv…

cs.CL2024

Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts

Youna Kim, Hyuhng Joon Kim, Cheonbok Park +6

When using large language models (LLMs) in knowledge-intensive tasks, such as open-domain question answering, external context can bridge the gap between external knowledge and the…

cs.CL2024★ 7 cited

HyperCLOVA X Technical Report

Kang Min Yoo, Jaegeun Han, Sookyo In +393

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…

cs.CL2024★ 3 cited

Aligning Language Models to Explicitly Handle Ambiguity

Hyuhng Joon Kim, Youna Kim, Cheonbok Park +5

In interactions between users and language model agents, user utterances frequently exhibit ellipsis (omission of words or phrases) or imprecision (lack of exactness) to prioritize…

cs.CL2024★ 1 cited

KMMLU: Measuring Massive Multitask Language Understanding in Korean

Guijin Son, Hanwool Lee, Sungdong Kim +6

We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. While prior Korean benchmarks are tr…