15 citations · 28 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty
Yongjin Yang, Haneul Yoo, Hwaran Lee
Despite the massive advancements in large language models (LLMs), they still suffer from producing plausible but incorrect responses. To improve the reliability of LLMs, recent res…
Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding
Haneul Yoo, Yongjin Yang, Hwaran Lee
As large language models (LLMs) have advanced rapidly, concerns regarding their safety have become prominent. In this paper, we discover that code-switching in red-teaming queries…
BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages
Junho Myung, Nayeon Lee, Yi Zhou +19
Large language models (LLMs) often lack culture-specific knowledge of daily life, especially across diverse regions and non-English languages. Existing benchmarks for evaluating LL…
TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models
Jaewoo Ahn, Taehyun Lee, Junyoung Lim +4
While Large Language Models (LLMs) can serve as agents to simulate human behaviors (i.e., role-playing agents), we emphasize the importance of point-in-time role-playing. This situ…
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…
Calibrating Large Language Models Using Their Generations Only
Dennis Ulmer, Martin Gubri, Hwaran Lee +2
As large language models (LLMs) are increasingly deployed in user-facing applications, building trust and maintaining safety by accurately quantifying a model's confidence in its p…
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
Martin Gubri, Dennis Ulmer, Hwaran Lee +2
Large Language Model (LLM) services and models often come with legal rules on who can use them and how they must use them. Assessing the compliance of the released LLMs is crucial,…