2 citations · 4 across the 11 of their papers we have counts for
12 papers
K-EXAONE 2.0 Technical Report
Eunbi Choi, Kibong Choi, Sehyun Chun +74
This technical report presents K-EXAONE 2.0, an open-weight multilingual foundation model developed by LG AI Research as a step in our effort toward global frontier-scale foundatio…
K-EXAONE Technical Report
Eunbi Choi, Kibong Choi, Seokhee Hong +62
This technical report presents K-EXAONE, a large-scale multilingual language model developed by LG AI Research. K-EXAONE is built on a Mixture-of-Experts architecture with 236B tot…
DEER: A Benchmark for Evaluating Deep Research Agents on Expert Report Generation
Janghoon Han, Heegyu Kim, Changho Lee +6
Recent advances in large language models have enabled deep research systems that generate expert-level reports through multi-step reasoning and evidence-based synthesis. However, e…
Spanning Tree Autoregressive Visual Generation
Sangkyu Lee, Changho Lee, Janghoon Han +6
We present Spanning Tree Autoregressive (STAR) modeling, which can incorporate prior knowledge of images, such as center bias and locality, to maintain sampling performance while a…
EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes
Kyunghoon Bae, Eunbi Choi, Kibong Choi +37
This technical report introduces EXAONE 4.0, which integrates a Non-reasoning mode and a Reasoning mode to achieve both the excellent usability of EXAONE 3.5 and the advanced reaso…
LGAI-EMBEDDING-Preview Technical Report
Jooyoung Choi, Hyun Kim, Hansol Jang +6
This report presents a unified instruction-based framework for learning generalized text embeddings optimized for both information retrieval (IR) and non-IR tasks. Built upon a dec…
EXAONE Deep: Reasoning Enhanced Language Models
Kyunghoon Bae, Eunbi Choi, Kibong Choi +28
We present EXAONE Deep series, which exhibits superior capabilities in various reasoning tasks, including math and coding benchmarks. We train our models mainly on the reasoning-sp…
KL Penalty Control via Perturbation for Direct Preference Optimization
Sangkyu Lee, Janghoon Han, Hosung Song +3
Direct Preference Optimization (DPO) demonstrates the advantage of aligning a large language model with human preference using only an offline dataset. However, DPO has the limitat…
EXAONE 3.0 7.8B Instruction Tuned Language Model
Soyoung An, Kyunghoon Bae, Eunbi Choi +34
We introduce EXAONE 3.0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research. Among different model size…
Deep Exploration of Cross-Lingual Zero-Shot Generalization in Instruction Tuning
Janghoon Han, Changho Lee, Joongbo Shin +3
Instruction tuning has emerged as a powerful technique, significantly boosting zero-shot performance on unseen tasks. While recent work has explored cross-lingual generalization by…
Instruction Matters: A Simple yet Effective Task Selection for Optimized Instruction Tuning of Specific Tasks
Changho Lee, Janghoon Han, Seonghyeon Ye +3
Instruction tuning has been proven effective in enhancing zero-shot generalization across various tasks and in improving the performance of specific tasks. For task-specific improv…
Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination
Nakyeong Yang, Taegwan Kang, Jungkyu Choi +2
Instruction-following language models often show undesirable biases. These undesirable biases may be accelerated in the real-world usage of language models, where a wide range of i…