2 citations · 5 across the 12 of their papers we have counts for
14 papers
Multimodal Safety Evaluation Should Measure Controllability Beyond Classification
Junhyeong Park, Hanwool Lee, DongGeon Lee +4
VLM safety is commonly evaluated through input- and output-level classification. Such classification is necessary, but it does not reveal whether a safety state is accessible or co…
NRT-Bench: Benchmarking Multi-Turn Red-Teaming of LLM Operator Agents in Safety-Critical Control Rooms
Hanwool Lee, Dasol Choi, Bokyeong Kim +2
Large language model (LLM) agents are increasingly proposed as supervisory components for safety-critical systems, yet their robustness under sustained, adaptive adversarial pressu…
What Users Leave Unsaid: Under-Specified Queries Limit Vision-Language Models
Dasol Choi, Guijin Son, Hanwool Lee +7
Current vision-language benchmarks predominantly feature well-structured questions with clear, explicit prompts. However, real user queries are often informal and underspecified. U…
AI PB: A Grounded Generative Agent for Personalized Investment Insights
Daewoo Park, Suho Park, Inseok Hong +5
We present AI PB, a production-scale generative agent deployed in real retail finance. Unlike reactive chatbots that answer queries passively, AI PB proactively generates grounded,…
NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance
Hanwool Lee, Sara Yu, Yewon Hwang +4
Financial text embeddings must distinguish changes in event status, perspective, and obligations even when passages share similar wording. NMIXX adapts existing encoders through 18…
TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts
Yewon Hwang, Sungbum Jung, Hanwool Lee +1
Domain specificity of embedding models is critical for effective performance. However, existing benchmarks, such as FinMTEB, are primarily designed for high-resource languages, lea…
(G)I-DLE: Generative Inference via Distribution-preserving Logit Exclusion with KL Divergence Minimization for Constrained Decoding
Hanwool Lee
We propose (G)I-DLE, a new approach to constrained decoding that leverages KL divergence minimization to preserve the intrinsic conditional probability distribution of autoregressi…
Redefining Evaluation Standards: A Unified Framework for Evaluating the Korean Capabilities of Language Models
Hanwool Lee, Dasol Choi, Sooyong Kim +6
Recent advancements in Korean large language models (LLMs) have driven numerous benchmarks and evaluation methods, yet inconsistent protocols cause up to 10 p.p performance gaps ac…
ML-Promise: A Multilingual Dataset for Corporate Promise Verification
Yohei Seki, Hakusen Shu, Anaïs Lhuissier +4
Promises made by politicians, corporate leaders, and public figures have a significant impact on public perception, trust, and institutional reputation. However, the complexity and…
IVE: Enhanced Probabilistic Forecasting of Intraday Volume Ratio with Transformers
Hanwool Lee, Heehwan Park
This paper presents a new approach to volume ratio prediction in financial markets, specifically targeting the execution of Volume-Weighted Average Price (VWAP) strategies. Recogni…
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…
HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models
Guijin Son, Hanwool Lee, Suwan Kim +6
Large language models (LLMs) trained on massive corpora demonstrate impressive capabilities in a wide range of tasks. While there are ongoing efforts to adapt these models to langu…
EaSyGuide : ESG Issue Identification Framework leveraging Abilities of Generative Large Language Models
Hanwool Lee, Jonghyun Choi, Sohyeon Kwon +1
This paper presents our participation in the FinNLP-2023 shared task on multi-lingual environmental, social, and corporate governance issue identification (ML-ESG). The task's obje…
Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance
Guijin Son, Hanwool Lee, Nahyeon Kang +1
Extraction of sentiment signals from news text, stock message boards, and business reports, for stock movement prediction, has been a rising field of interest in finance. Building…