Posts by Collection

portfolio

Portfolio item number 1

Short description of portfolio item number 1

Portfolio item number 2

Short description of portfolio item number 2

publications

LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop Advertising

Published in Proceedings of the 30th ACM international conference on information & knowledge management (CIKM), 2021

In this work, we present a lightweight yet accurate deep learning-based method to predict taxicabs’ next locations to better prepare for targeted advertising based on demographic information of locations.

Download Paper

What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers

Published in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021

We introduce HyperCLOVA, a Korean variant of 82B GPT-3 trained on a Korean-centric corpus of 560B tokens. Enhanced by our Korean-specific tokenization, HyperCLOVA with our training configuration shows state-of-the-art in-context zero-shot and few-shot learning performances on various downstream tasks in Korean.

Download Paper

Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models

Published in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), 2022

In this work, we study the challenge of imposing roles on open-domain dialogue systems, with the goal of making the systems maintain consistent roles while conversing naturally with humans.

Download Paper

Keep Me Updated! Memory Management in Long-term Conversations

Published in Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

We present a novel task and a corresponding dataset of memory management in long-term conversations, in which bots keep track of and bring up the latest information about users while conversing through multiple sessions.

Download Paper

Aligning Large Language Models through Synthetic Feedback

Published in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023

We propose a novel alignment learning framework with synthetic feedback not dependent on extensive human annotations and proprietary LLMs.

Download Paper

HyperCLOVA X Technical Report

Published in arXiv preprint, 2024

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. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI.

Download Paper