7 citations · 13 across the 14 of their papers we have counts for
12 papers · 1 filter
Who Pays More for Safety? Measuring the Disparate Cost of Safety Alignment across Languages
Chanwoong Yoon, Jungsoo Park, Alan Ritter
Safety alignment helps models adhere to human values, but it often reduces response utility. We ask a critical but understudied question: Does safety alignment impose the cost equa…
Soft Token Alignment for Cross-Lingual Reasoning
Jiayi He, Jungsoo Park, Wei Xu +1
Multilingual large language models often produce inconsistent reasoning and answers for semantically equivalent prompts in different languages. Prior work suggests that intermediat…
Safe and Scalable Web Agent Learning via Recreated Websites
Hyungjoo Chae, Jungsoo Park, Alan Ritter
Training autonomous web agents is fundamentally limited by the environments they learn from: real-world websites are unsafe to explore, hard to reset, and rarely provide verifiable…
Anticipatory Evaluation of Language Models: Predicting LLM Benchmark Scores from Descriptions
Jungsoo Park, Ethan Mendes, Gabriel Stanovsky +1
Modern ML research increasingly faces an evaluation bottleneck: building benchmarks and running experiments is costly, slowing iteration. We ask how much can be inferred about eval…
Can LLMs Help Uncover Insights about LLMs? A Large-Scale, Evolving Literature Analysis of Frontier LLMs
Jungsoo Park, Junmo Kang, Gabriel Stanovsky +1
The surge of LLM studies makes synthesizing their findings challenging. Analysis of experimental results from literature can uncover important trends across studies, but the time-c…
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…
Empowering Sentence Encoders with Prompting and Label Retrieval for Zero-shot Text Classification
Jimin Hong, Jungsoo Park, Daeyoung Kim +3
With contrastive pre-training, sentence encoders are generally optimized to locate semantically similar samples closer to each other in their embedding spaces. In this work, we foc…
Optimizing Test-Time Query Representations for Dense Retrieval
Mujeen Sung, Jungsoo Park, Jaewoo Kang +2
Recent developments of dense retrieval rely on quality representations of queries and contexts from pre-trained query and context encoders. In this paper, we introduce TOUR (Test-T…
FaVIQ: FAct Verification from Information-seeking Questions
Jungsoo Park, Sewon Min, Jaewoo Kang +2
Despite significant interest in developing general purpose fact checking models, it is challenging to construct a large-scale fact verification dataset with realistic real-world cl…
Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering
Gangwoo Kim, Hyunjae Kim, Jungsoo Park +1
One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do no…
Consistency Training with Virtual Adversarial Discrete Perturbation
Jungsoo Park, Gyuwan Kim, Jaewoo Kang
Consistency training regularizes a model by enforcing predictions of original and perturbed inputs to be similar. Previous studies have proposed various augmentation methods for th…
Adversarial Subword Regularization for Robust Neural Machine Translation
Jungsoo Park, Mujeen Sung, Jinhyuk Lee +1
Exposing diverse subword segmentations to neural machine translation (NMT) models often improves the robustness of machine translation as NMT models can experience various subword…