8 citations · 27 across the 11 of their papers we have counts for
11 papers
OMHBench: Benchmarking Balanced and Grounded Omni-Modal Multi-Hop Reasoning
Seunghee Kim, Ingyu Bang, Seokgyu Jang +5
Multimodal Large Language Models (MLLMs) have increasingly supported omni-modal processing across text, vision, and speech. However, existing evaluation frameworks for such models…
Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models
Cheonbok Park, Jeonghoon Kim, Joosung Lee +3
Reinforcement learning with verifiable reward (RLVR) has been instrumental in eliciting strong reasoning capabilities from large language models (LLMs) via long chains of thought (…
Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning
Sanghwan Bae, Jiwoo Hong, Min Young Lee +3
Recent advances in reinforcement learning with verifiable rewards (RLVR) show that large language models enhance their reasoning abilities when trained with verifiable signals. How…
HyperCLOVA X Technical Report
Kang Min Yoo, Jaegeun Han, Sookyo In +393
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. H…
Revealing User Familiarity Bias in Task-Oriented Dialogue via Interactive Evaluation
Takyoung Kim, Jamin Shin, Young-Ho Kim +2
Most task-oriented dialogue (TOD) benchmarks assume users that know exactly how to use the system by constraining the user behaviors within the system's capabilities via strict use…
Aligning Large Language Models through Synthetic Feedback
Sungdong Kim, Sanghwan Bae, Jamin Shin +4
Aligning large language models (LLMs) to human values has become increasingly important as it enables sophisticated steering of LLMs. However, it requires significant human demonst…
Keep Me Updated! Memory Management in Long-term Conversations
Sanghwan Bae, Donghyun Kwak, Soyoung Kang +7
Remembering important information from the past and continuing to talk about it in the present are crucial in long-term conversations. However, previous literature does not deal wi…
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models
Sanghwan Bae, Donghyun Kwak, Sungdong Kim +4
Recent open-domain dialogue models have brought numerous breakthroughs. However, building a chat system is not scalable since it often requires a considerable volume of human-human…
Summary Level Training of Sentence Rewriting for Abstractive Summarization
Sanghwan Bae, Taeuk Kim, Jihoon Kim +1
As an attempt to combine extractive and abstractive summarization, Sentence Rewriting models adopt the strategy of extracting salient sentences from a document first and then parap…
SNU_IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational Classification
Sanghwan Bae, Jihun Choi, Sang-goo Lee
We present several techniques to tackle the mismatch in class distributions between training and test data in the Contextual Emotion Detection task of SemEval 2019, by extending th…
Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag Representations
Taeuk Kim, Jihun Choi, Daniel Edmiston +2
Most existing recursive neural network (RvNN) architectures utilize only the structure of parse trees, ignoring syntactic tags which are provided as by-products of parsing. We pres…