12 citations · 20 across the 12 of their papers we have counts for
13 papers
Scaling Creative Writing Beyond Story-Centric Data with Attribute-Guided Genre Expansion
Hwan Chang, Yongil Kim, Heuiyeen Yeen +3
High-quality creative writing data for large language models (LLMs) remains dominated by story-centric data, limiting models' ability to follow the structural and functional conven…
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…
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…
Reasoning Models Better Express Their Confidence
Dongkeun Yoon, Seungone Kim, Sohee Yang +6
Despite their strengths, large language models (LLMs) often fail to communicate their confidence accurately, making it difficult to assess when they might be wrong and limiting the…
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…
EXAONE 3.5: Series of Large Language Models for Real-world Use Cases
Soyoung An, Kyunghoon Bae, Eunbi Choi +29
This technical report introduces the EXAONE 3.5 instruction-tuned language models, developed and released by LG AI Research. The EXAONE 3.5 language models are offered in three con…
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…
Block Transformer: Global-to-Local Language Modeling for Fast Inference
Namgyu Ho, Sangmin Bae, Taehyeon Kim +6
We introduce the Block Transformer which adopts hierarchical global-to-local modeling to autoregressive transformers to mitigate the inference bottlenecks associated with self-atte…
ListT5: Listwise Reranking with Fusion-in-Decoder Improves Zero-shot Retrieval
Soyoung Yoon, Eunbi Choi, Jiyeon Kim +3
We propose ListT5, a novel reranking approach based on Fusion-in-Decoder (FiD) that handles multiple candidate passages at both train and inference time. We also introduce an effic…
Cross-lingual QA: A Key to Unlocking In-context Cross-lingual Performance
Sunkyoung Kim, Dayeon Ki, Yireun Kim +1
Multilingual large language models (MLLMs) have demonstrated significant cross-lingual capabilities through in-context learning. Existing approaches typically construct monolingual…
Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction Following
Seonghyeon Ye, Hyeonbin Hwang, Sohee Yang +3
In this paper, we present our finding that prepending a Task-Agnostic Prefix Prompt (TAPP) to the input improves the instruction-following ability of various Large Language Models…
External Knowledge Selection with Weighted Negative Sampling in Knowledge-grounded Task-oriented Dialogue Systems
Janghoon Han, Joongbo Shin, Hosung Song +4
Constructing a robust dialogue system on spoken conversations bring more challenge than written conversation. In this respect, DSTC10-Track2-Task2 is proposed, which aims to build…