7.5k citations · 7.7k across the 32 of their papers we have counts for
23 papers · 1 filter
Generative Prompt Internalization
Haebin Shin, Lei Ji, Yeyun Gong +3
Prompts used in recent large language model based applications are often fixed and lengthy, leading to significant computational overhead. To address this challenge, we propose Gen…
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…
KMMLU: Measuring Massive Multitask Language Understanding in Korean
Guijin Son, Hanwool Lee, Sungdong Kim +6
We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. While prior Korean benchmarks are tr…
LangBridge: Multilingual Reasoning Without Multilingual Supervision
Dongkeun Yoon, Joel Jang, Sungdong Kim +3
We introduce LangBridge, a zero-shot approach to adapt language models for multilingual reasoning tasks without multilingual supervision. LangBridge operates by bridging two models…
Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models
Gangwoo Kim, Sungdong Kim, Byeongguk Jeon +2
Questions in open-domain question answering are often ambiguous, allowing multiple interpretations. One approach to handling them is to identify all possible interpretations of the…
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
Seungone Kim, Jamin Shin, Yejin Cho +8
Recently, using a powerful proprietary Large Language Model (LLM) (e.g., GPT-4) as an evaluator for long-form responses has become the de facto standard. However, for practitioners…
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
Seonghyeon Ye, Doyoung Kim, Sungdong Kim +6
Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on…
Gradient Ascent Post-training Enhances Language Model Generalization
Dongkeun Yoon, Joel Jang, Sungdong Kim +1
In this work, we empirically show that updating pretrained LMs (350M, 1.3B, 2.7B) with just a few steps of Gradient Ascent Post-training (GAP) on random, unlabeled text corpora enh…
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…
Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild?
Sang-Woo Lee, Sungdong Kim, Donghyeon Ko +9
Task-oriented dialogue (TOD) systems are mainly based on the slot-filling-based TOD (SF-TOD) framework, in which dialogues are broken down into smaller, controllable units (i.e., s…
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…
Leveraging Pre-Trained Language Models to Streamline Natural Language Interaction for Self-Tracking
Young-Ho Kim, Sungdong Kim, Minsuk Chang +1
Current natural language interaction for self-tracking tools largely depends on bespoke implementation optimized for a specific tracking theme and data format, which is neither gen…
On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Model
Seongjin Shin, Sang-Woo Lee, Hwijeen Ahn +8
Many recent studies on large-scale language models have reported successful in-context zero- and few-shot learning ability. However, the in-depth analysis of when in-context learni…
Generating Information-Seeking Conversations from Unlabeled Documents
Gangwoo Kim, Sungdong Kim, Kang Min Yoo +1
In this paper, we introduce a novel framework, SIMSEEK, (Simulating information-Seeking conversation from unlabeled documents), and compare its two variants. In our baseline SIMSEE…
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…
Saving Dense Retriever from Shortcut Dependency in Conversational Search
Sungdong Kim, Gangwoo Kim
Conversational search (CS) needs a holistic understanding of conversational inputs to retrieve relevant passages. In this paper, we demonstrate the existence of a retrieval shortcu…
Can Language Models be Biomedical Knowledge Bases?
Mujeen Sung, Jinhyuk Lee, Sean Yi +3
Pre-trained language models (LMs) have become ubiquitous in solving various natural language processing (NLP) tasks. There has been increasing interest in what knowledge these LMs…
NeuralWOZ: Learning to Collect Task-Oriented Dialogue via Model-Based Simulation
Sungdong Kim, Minsuk Chang, Sang-Woo Lee
We propose NeuralWOZ, a novel dialogue collection framework that uses model-based dialogue simulation. NeuralWOZ has two pipelined models, Collector and Labeler. Collector generate…
KLUE: Korean Language Understanding Evaluation
Sungjoon Park, Jihyung Moon, Sungdong Kim +28
We introduce Korean Language Understanding Evaluation (KLUE) benchmark. KLUE is a collection of 8 Korean natural language understanding (NLU) tasks, including Topic Classification,…
Efficient Dialogue State Tracking by Selectively Overwriting Memory
Sungdong Kim, Sohee Yang, Gyuwan Kim +1
Recent works in dialogue state tracking (DST) focus on an open vocabulary-based setting to resolve scalability and generalization issues of the predefined ontology-based approaches…
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim +4
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting va…
QADiver: Interactive Framework for Diagnosing QA Models
Gyeongbok Lee, Sungdong Kim, Seung-won Hwang
Question answering (QA) extracting answers from text to the given question in natural language, has been actively studied and existing models have shown a promise of outperforming…