14 citations · 18 across the 7 of their papers we have counts for
8 papers
Q-TIE: A Lightweight and Generalizable Re-ranking Framework for Temporal Information Retrieval
Soyeon Kim, Hyunjin Kim, JinYeong Bak +1
Temporal Information Retrieval (TIR) has been increasingly critical given the rise of Retrieval-Augmented Generation (RAG). Since temporally mismatched evidence can be highly misle…
When2Think: Learning When and How Much to Reason
Jaejun Shim, HyunJin Kim, Young Jin Kim +1
Large Reasoning Models (LRMs) often overthink easy problems and underthink hard ones, leading to inefficient computation allocation. Existing methods regulate generated computation…
Temporal Preference Optimization for Unsupervised Retrieval
HyunJin Kim, Jaejun Shim, Young Jin Kim +1
Unsupervised dense retrievers offer scalability by learning semantic similarity from unlabeled documents via contrastive learning, but they struggle to capture the temporal relevan…
Research Superalignment Should Advance Now with Alternating Competence and Conformity Optimization
HyunJin Kim, Xiaoyuan Yi, Jing Yao +4
The recent leap in AI capabilities, driven by big generative models, has sparked the possibility of achieving Artificial General Intelligence (AGI) and further triggered discussion…
Talking with Tables for Better LLM Factual Data Interactions
Jio Oh, Geon Heo, Seungjun Oh +5
Large Language Models (LLMs) often struggle with requests related to information retrieval and data manipulation that frequently arise in real-world scenarios under multiple condit…
The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment
HyunJin Kim, DongHyun Ryu, Xiaoyuan Yi +6
The emergence of large language models (LLMs) has sparked discussion on Artificial Superintelligence (ASI), a hypothetical AI system that surpasses human intelligence. Although ASI…
Artificial Intelligence and Strategic Decision-Making: Evidence from Entrepreneurs and Investors
Felipe A. Csaszar, Harsh Ketkar, Hyunjin Kim
This paper explores how artificial intelligence (AI) may impact the strategic decision-making (SDM) process in firms. We illustrate how AI could augment existing SDM tools and prov…
Can Separators Improve Chain-of-Thought Prompting?
Yoonjeong Park, Hyunjin Kim, Chanyeol Choi +2
Chain-of-thought (CoT) prompting is a simple and effective method for improving the reasoning capabilities of Large Language Models (LLMs). The basic idea of CoT is to let LLMs bre…