20 citations · 22 across the 11 of their papers we have counts for
11 papers
MULTI3IR: A Benchmark for Multi-perspective Multi-domain Multi-modal Information Retrieval
Seokwon Song, Sohyeon Kim, Gunhee Kim
Information retrieval (IR) increasingly targets open-ended queries that admit diverse perspectives. Existing IR benchmarks, however, focus primarily on closed-ended queries, while…
When Is Enough Not Enough? Illusory Completion in Search Agents
Dayoon Ko, Jihyuk Kim, Sohyeon Kim +5
In agentic search, an LLM agent searches the web, reads the pages it finds, and decides what to look for next before returning an answer. But can we trust an answer simply because…
Hybrid Deep Searcher: Scalable Parallel and Sequential Search Reasoning
Dayoon Ko, Jihyuk Kim, Haeju Park +6
Large reasoning models (LRMs) combined with retrieval-augmented generation (RAG) have enabled deep research agents capable of multi-step reasoning with external knowledge retrieval…
When Should Dense Retrievers Be Updated in Evolving Corpora? Detecting Out-of-Distribution Corpora Using GradNormIR
Dayoon Ko, Jinyoung Kim, Sohyeon Kim +5
Dense retrievers encode texts into embeddings to efficiently retrieve relevant documents from large databases in response to user queries. However, real-world corpora continually e…
Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning
Jaehyeon Son, Soochan Lee, Gunhee Kim
Recent studies have shown that Transformers can perform in-context reinforcement learning (RL) by imitating existing RL algorithms, enabling sample-efficient adaptation to unseen t…
DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG
Jinyoung Kim, Dayoon Ko, Gunhee Kim
In the rapidly evolving landscape of language, resolving new linguistic expressions in continuously updating knowledge bases remains a formidable challenge. This challenge becomes…
GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge?
Dayoon Ko, Jinyoung Kim, Hahyeon Choi +1
In the real world, knowledge is constantly evolving, which can render existing knowledge-based datasets outdated. This unreliability highlights the critical need for continuous upd…
Learning to Continually Learn with the Bayesian Principle
Soochan Lee, Hyeonseong Jeon, Jaehyeon Son +1
In the present era of deep learning, continual learning research is mainly focused on mitigating forgetting when training a neural network with stochastic gradient descent on a non…
When Meta-Learning Meets Online and Continual Learning: A Survey
Jaehyeon Son, Soochan Lee, Gunhee Kim
Over the past decade, deep neural networks have demonstrated significant success using the training scheme that involves mini-batch stochastic gradient descent on extensive dataset…
Can Language Models Laugh at YouTube Short-form Videos?
Dayoon Ko, Sangho Lee, Gunhee Kim
As short-form funny videos on social networks are gaining popularity, it becomes demanding for AI models to understand them for better communication with humans. Unfortunately, pre…
Recasting Continual Learning as Sequence Modeling
Soochan Lee, Jaehyeon Son, Gunhee Kim
In this work, we aim to establish a strong connection between two significant bodies of machine learning research: continual learning and sequence modeling. That is, we propose to…