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20212026
most citedHyperCLOVA X Technical Report

7 citations · 11 across the 8 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.LG2024

DSAI: Unbiased and Interpretable Latent Feature Extraction for Data-Centric AI

Hyowon Cho, Soonwon Ka, Daechul Park +3

Large language models (LLMs) often struggle to objectively identify latent characteristics in large datasets due to their reliance on pre-trained knowledge rather than actual data…

cs.CL2024

Culinary Class Wars: Evaluating LLMs using ASH in Cuisine Transfer Task

Hoonick Lee, Mogan Gim, Donghyeon Park +2

The advent of Large Language Models (LLMs) have shown promise in various creative domains, including culinary arts. However, many LLMs still struggle to deliver the desired level o…

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★ 2 cited

Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models

Yukyung Lee, Soonwon Ka, Bokyung Son +2

Large Language Models (LLMs) have impacted the writing process, enhancing productivity by collaborating with humans in content creation platforms. However, generating high-quality,…

cs.CL2024

CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists

Yukyung Lee, Joonghoon Kim, Jaehee Kim +4

Existing LLM-as-a-Judge approaches for evaluating text generation suffer from rating inconsistencies, with low agreement and high rating variance across different evaluator models.…