3 citations · 3 across the 11 of their papers we have counts for
11 papers
MoNe: Modular Neural Memory for Efficient Long Context Inference
Wonguk Cho, Kyubyung Chae, Tribhuvanesh Orekondy +6
We present MoNe, a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference without retraining. MoNe reads context in f…
Beyond Case Law: Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA
Kyubyung Chae, Jewon Yeom, Jeongjae Park +5
Legal QA benchmarks have predominantly focused on case law, overlooking the unique challenges of statute-centric regulatory reasoning. In statutory domains, relevant evidence is di…
What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say "I Don't Know"
Joosung Lee, Hwiyeol Jo, Donghyeon Ko +3
While large language models (LLMs) demonstrate strong capabilities across diverse user queries, they still suffer from hallucinations, often arising from knowledge misalignment bet…
Robust Domain Generalization under Divergent Marginal and Conditional Distributions
Jewon Yeom, Kyubyung Chae, Hyunggyu Lim +3
Domain generalization (DG) aims to learn predictive models that can generalize to unseen domains. Most existing DG approaches focus on learning domain-invariant representations und…
What If TSF: Reframing Time Series Forecasting as Scenario-Guided Multimodal Forecasting
Jinkwan Jang, Hyunbin Jin, Hyungjin Park +2
Recent advances in large language models (LLMs) have enabled time series forecasting to move beyond numerical observations and incorporate external information in multimodal settin…
KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs
Donghyeon Ko, Yeguk Jin, Kyubyung Chae +6
We present , a benchmark for evaluating factuality in large language models (LLMs) with a focus on Korean cultural knowledge. KoSimpleQA is d…
Assessing Socio-Cultural Alignment and Technical Safety of Sovereign LLMs
Kyubyung Chae, Gihoon Kim, Gyuseong Lee +3
Recent trends in LLMs development clearly show growing interest in the use and application of sovereign LLMs. The global debate over sovereign LLMs highlights the need for governme…
From Threat to Tool: Leveraging Refusal-Aware Injection Attacks for Safety Alignment
Kyubyung Chae, Hyunbin Jin, Taesup Kim
Safely aligning large language models (LLMs) often demands extensive human-labeled preference data, a process that's both costly and time-consuming. While synthetic data offers a p…
When Vision Models Meet Parameter Efficient Look-Aside Adapters Without Large-Scale Audio Pretraining
Juan Yeo, Jinkwan Jang, Kyubyung Chae +2
Recent studies show that pretrained vision models can boost performance in audio downstream tasks. To enhance the performance further, an additional pretraining stage with large sc…
Model-based Preference Optimization in Abstractive Summarization without Human Feedback
Jaepill Choi, Kyubyung Chae, Jiwoo Song +2
In abstractive summarization, the challenge of producing concise and accurate summaries arises from the vast amount of information contained in the source document. Consequently, a…
Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information
Kyubyung Chae, Jaepill Choi, Yohan Jo +1
A primary challenge in abstractive summarization is hallucination -- the phenomenon where a model generates plausible text that is absent in the source text. We hypothesize that th…