8 papers
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…
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…
NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance
Hanwool Lee, Sara Yu, Yewon Hwang +4
General-purpose sentence embedding models often struggle to capture specialized financial semantics, especially in low-resource languages like Korean, due to domain-specific jargon…
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,…
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…