Papers by Chanjun Park

Where am I? Large Language Models Wandering between Semantics and Structures in Long Contexts (2024.emnlp-main)

Challenge:	Creating high-quality datasets for large language models often relies on resource-intensive, GPU-accelerated models for quality filtering, making the process time-consuming and costly.
Approach:	They propose a framework that operates entirely on CPUs to streamline the processes of dataset extraction, filtering, and curation.
Outcome:	The proposed framework reduces preparation time and costs while maintaining high data quality while enhancing the applicability of LLMs in specialized contexts.

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Seonmin Koo, Jinsung Kim, YoungJoon Jang, Chanjun Park, Heuiseok Lim

Length-aware Byte Pair Encoding for Mitigating Over-segmentation in Korean Machine Translation (2024.findings-acl)

Challenge:	Existing evaluations of the open-domain question answering task focus solely on whether the model provides the correct answer.
Approach:	They propose to examine the phenomenon of discrepancies in abilities across two distinct tasks—QA and evidence selection—when performed simultaneously.
Outcome:	The proposed framework and resources examines the ability of large language models to perform two distinct tasks simultaneously, from the perspective of task alignment.

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Jungseob Lee, Hyeonseok Moon, Seungjun Lee, Chanjun Park, Sugyeong Eo, Hyunwoong Ko, Jaehyung Seo, Seungyoon Lee, Heuiseok Lim

Machine Translation

Challenge:	Byte Pair Encoding (BPE) is an effective approach in machine translation across several languages, but it is prone to over-segmentation in Korean, an agglutinative and morphologically rich language.
Approach:	They propose a new method that incorporates long words into the Korean vocabulary by strategically preserving morphological information and reducing semantic confusion.
Outcome:	The proposed method outperforms BPE and surpasses state-of-the-art morpheme-aware tokenization methods.

LangSAE Editing: Improving Multilingual Information Retrieval via Post-hoc Language Identity Removal (2026.acl-long)

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Dongjun Kim, Jeongho Yoon, Chanjun Park, Heuiseok Lim

Can Code-Switched Texts Activate a Knowledge Switch in LLMs? A Case Study on English-Korean Code-Switching (2025.findings-emnlp)

Challenge:	Existing methods for dense retrieval in multilingual environments encode language identity alongside semantics.
Approach:	They propose a method that trains on pooled embeddings to remove language-identity signal directly in vector space.
Outcome:	The proposed method improves ranking quality and cross-language coverage across multiple languages with especially strong gains for script-distinct languages.

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Seoyeon Kim, Huiseo Kim, Chanjun Park, Jinyoung Yeo, Dongha Lee

Multilinguality

Challenge:	Recent large language models (LLMs) demonstrate multilingual abilities, yet they are English-centric due to dominance of English in training corpora.
Approach:	They propose to use a synthetic English-korean CS question-answering dataset to investigate this potential.
Outcome:	The proposed model can activate, identify and leverage knowledge for reasoning in low-resource languages.

PEEP-Talk: A Situational Dialogue-based Chatbot for English Education (2023.acl-demo)

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Seungjun Lee, Yoonna Jang, Chanjun Park, Jungseob Lee, Jaehyung Seo, Hyeonseok Moon, Sugyeong Eo, Seounghoon Lee, Bernardo Yahya, Heuiseok Lim

Natural Language Interfaces

Challenge:	Existing chatbots lack realistic practice scenarios for English learners . existing platforms employ hand-crafted and patternmatching rules, limiting communication ability and responding appropriately to out-of-situation utterances.
Approach:	They propose a real-world situational dialogue-based chatbot for English education . it generates appropriate responses in various real-life situations while providing accurate feedback .
Outcome:	The proposed chatbot generates appropriate responses in various real-life situations while providing accurate feedback to learners.

Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval (2025.findings-acl)

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Yongchan Chun, Minhyuk Kim, Dongjun Kim, Chanjun Park, Heuiseok Lim

Syntactic Text Processing

Challenge:	Large language models (LLMs) have improved IE, but their potential for ATE has not been explored.
Approach:	They propose a retrieval-based prompting strategy that selects demonstrations according to syntactic rather than semantic similarity in a few-shot setting.
Outcome:	The proposed method improves performance on three specialized ATE benchmarks.

CoME: An Unlearning-based Approach to Conflict-free Model Editing (2025.naacl-long)

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Dahyun Jung, Jaehyung Seo, Jaewook Lee, Chanjun Park, Heuiseok Lim

Evalverse: Unified and Accessible Library for Large Language Model Evaluation (2024.emnlp-demo)

Challenge:	Large language models (LLMs) often retain outdated or incorrect information from pre-training, which undermines their reliability.
Approach:	They propose a conflict-free model editing framework that selectively removes outdated knowledge from LLMs to improve their accuracy and reliability.
Outcome:	The proposed framework improves both editing accuracy and model reliability when applied to existing editing methods.

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Jihoo Kim, Wonho Song, Dahyun Kim, Yunsu Kim, Yungi Kim, Chanjun Park

KoCommonGEN v2: A Benchmark for Navigating Korean Commonsense Reasoning Challenges in Large Language Models (2024.findings-acl)

Challenge:	Evalverse is a library that unifies disparate evaluation tools into a single, user-friendly framework.
Approach:	They propose to integrate existing evaluation frameworks into a single, user-friendly framework that enables individuals with limited knowledge of artificial intelligence to request LLM evaluations and receive detailed reports.
Outcome:	The proposed framework can be used by individuals with limited knowledge of artificial intelligence to request and receive LLM evaluations and receive detailed reports.

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Jaehyung Seo, Jaewook Lee, Chanjun Park, SeongTae Hong, Seungjun Lee, Heuiseok Lim

Reasoning

Challenge:	Language models are striving to grasp commonsense reasoning, but they are lacking in Korean commons- ense benchmarks.
Approach:	They present a fine-grained benchmark dataset focused on Korean commonsense reasoning that includes multiple-choice questions across seven error categories.
Outcome:	The proposed datasets show that LLMs struggle with Korean commonsense reasoning . human accuracy benchmarked at approximately 85%, while GPT-4’s performance lags at about 74%, and other LLM models demonstrate an average accuracy of around 42%.

KEBAP: Korean Error Explainable Benchmark Dataset for ASR and Post-processing (2023.emnlp-main)

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Seonmin Koo, Chanjun Park, Jinsung Kim, Jaehyung Seo, Sugyeong Eo, Hyeonseok Moon, Heuiseok Lim

Multimodality

Challenge:	Conventional evaluation metrics for automatic speech recognition systems produce a singular aggregate score, which is insufficient for understanding specific system vulnerabilities.
Approach:	They propose to introduce the Korean Error Explainable Benchmark Dataset for ASR and Post-processing (KEBAP) this method enables a more balanced assessment encompassing speech recognition accuracy and user readability.
Outcome:	The proposed method enables a more balanced assessment encompassing speech recognition accuracy and user readability.

Open Ko-LLM Leaderboard: Evaluating Large Language Models in Korean with Ko-H5 Benchmark (2024.acl-long)

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Chanjun Park, Hyeonwoo Kim, Dahyun Kim, SeongHwan Cho, Sanghoon Kim, Sukyung Lee, Yungi Kim, Hwalsuk Lee

ZEBRA: Leveraging Model-Behavioral Knowledge for Zero-Annotation Preference Dataset Construction (2025.findings-emnlp)

Challenge:	Existing benchmarks for evaluating Large Language Models are limited to the English language.
Approach:	They introduce the Open Ko-LLM Leaderboard and Ko-H5 Benchmark as tools for evaluating Large Language Models in Korean using private test sets.
Outcome:	The proposed evaluation framework is well integrated in the Korean LLM community.

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Jeesu Jung, Chanjun Park, Sangkeun Jung

Challenge:	Recent efforts in LLM alignment focus on instance-wise supervision, costing substantial . ZEBRA binarizes response pairs by evaluating the quality and similarity of their origin models .
Approach:	They propose a model behavior-wise zero-annotation framework that binarizes preference data . ZEBRA binarized response pairs by evaluating the quality and similarity of their origin models .
Outcome:	The proposed framework achieves comparable alignment performance to instance-supervised methods .

Exploring Inherent Biases in LLMs within Korean Social Context: A Comparative Analysis of ChatGPT and GPT-4 (2024.naacl-srw)

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Seungyoon Lee, Dong Kim, Dahyun Jung, Chanjun Park, Heuiseok Lim

Benchmark Profiling: Mechanistic Diagnosis of LLM Benchmarks (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) have been criticized for perpetuating stereotypes against diverse groups based on race, sexual orientation, and other attributes.
Approach:	They devised a set of prompts that reflect major societal issues in Korea and assign varied personas to both ChatGPT and GPT-4 to assess the toxicity of the generated sentences.
Outcome:	The proposed model produces twice the level of toxic content as ChatGPT and GPT-4 under certain conditions.

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Dongjun Kim, Gyuho Shim, Yongchan Chun, Minhyuk Kim, Chanjun Park, Heuiseok Lim

Challenge:	Large Language Models are often judged by their scores on standard benchmarks, yet such scores often overstate real capability since they mask the mix of skills a task actually demands.
Approach:	They propose a diagnostic framework that decomposes benchmark performance into ten cognitively grounded abilities and computes an Ability Impact Score (AIS) AIS quantifies how much each ability contributes to a model’s success on a given benchmark.
Outcome:	The proposed framework decomposes performance into ten cognitively grounded abilities and computes an Ability Impact Score (AIS) that quantifies how much each ability contributes to a model’s success on a given benchmark.

CHEF in the Language Kitchen: A Generative Data Augmentation Leveraging Korean Morpheme Ingredients (2023.emnlp-main)

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Jaehyung Seo, Hyeonseok Moon, Jaewook Lee, Sugyeong Eo, Chanjun Park, Heuiseok Lim

FLEX: A Benchmark for Evaluating Robustness of Fairness in Large Language Models (2025.findings-naacl)

Challenge:	Korean morphological variations present unique opportunities and challenges in natural language processing (NLP), necessitating an advanced understanding of morpheme-based sentence construction.
Approach:	They propose a method to replicate morphological transformations inherent in Korean sentences based on lexical and functional morphemes through generative data augmentation.
Outcome:	The proposed method improves performance in Korean multiple classification datasets without incurring external data usage.

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Dahyun Jung, Seungyoon Lee, Hyeonseok Moon, Chanjun Park, Heuiseok Lim

Priming Ancient Korean Neural Machine Translation (2022.lrec-1)

Challenge:	Existing safety evaluations may overlook the inherent weaknesses of Large Language Models, despite their benefits.
Approach:	They propose a benchmark to evaluate the robustness of Large Language Models under extreme conditions.
Outcome:	The proposed approach evaluates the fairness of large language models under extreme conditions.

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Chanjun Park, Seolhwa Lee, Jaehyung Seo, Hyeonseok Moon, Sugyeong Eo, Heuiseok Lim

Multilinguality

Challenge:	Recent studies have focused on the restoration and translation of historical languages.
Approach:	They propose to use two different stimuli to priming ancient-Korean NMT . they confirm the possibility of developing a human-centric model based on cognitive science .
Outcome:	The proposed model can be used to translate historical Korean documents using neural machine translation.

Should we find another model?: Improving Neural Machine Translation Performance with ONE-Piece Tokenization Method without Model Modification (2021.naacl-industry)

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Chanjun Park, Sugyeong Eo, Hyeonseok Moon, Heuiseok Lim

Syntactic Text Processing

Challenge:	Recent studies using pretrain-finetuning approach have achieved state-of-the-art (SOTA) performance in many natural language processing tasks.
Approach:	They propose a new tokenization method that combines morphology-considered subword tokenization and vocabulary methods to address this limitation.
Outcome:	The proposed method can be used without modifying the model structure.

Hyper-BTS Dataset: Scalability and Enhanced Analysis of Back TranScription (BTS) for ASR Post-Processing (2024.findings-eacl)

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Chanjun Park, Jaehyung Seo, Seolhwa Lee, Junyoung Son, Hyeonseok Moon, Sugyeong Eo, Chanhee Lee, Heuiseok Lim

Multimodality

Challenge:	Automatic Speech Recognition (ASR) post-processing requires substantial amounts of data, requiring expensive phonetic transcription experts.
Approach:	They propose a "Hyper-BTS" dataset that is five times larger than prior studies . they propose criteria for categorizing error types within ASR post-processing .
Outcome:	The proposed method can generate ASR inputs from clean text using a text-to-speech system.

Exploring Coding Spot: Understanding Parametric Contributions to LLM Coding Performance (2026.findings-acl)

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Dongjun Kim, Minhyuk Kim, Yongchan Chun, Chanjun Park, Heuiseok Lim

Multimodality

Challenge:	Large Language Models (LLMs) have demonstrated proficiency in code generation and comprehension across multiple programming languages.
Approach:	They propose a parameter-localized subset of LLMs that facilitates coding capabilities.
Outcome:	The proposed model significantly improves performance on coding tasks while preserving non-coding functionalities.

Representing the Under-Represented: Cultural and Core Capability Benchmarks for Developing Thai Large Language Models (2025.coling-main)

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Dahyun Kim, Sukyung Lee, Yungi Kim, Attapol Rutherford, Chanjun Park

Generative Interpretation: Toward Human-Like Evaluation for Educational Question-Answer Pair Generation (2024.findings-eacl)

Challenge:	Rapid advancements in large language models have highlighted the need for robust evaluation frameworks that assess their core capabilities.
Approach:	They propose two benchmarks to assess core capabilities of large language models . current benchmarks for Thai focus mainly on traditional NLP tasks .
Outcome:	The proposed benchmarks are based on evaluations of various LLMs with multi-lingual capabilities and are publicly available to encourage further research and development for Thai LLM.

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Hyeonseok Moon, Jaewook Lee, Sugyeong Eo, Chanjun Park, Jaehyung Seo, Heuiseok Lim

Explainability & Interpretability in NLP

Challenge:	Existing evaluation methods often fail to produce objective results and favor high similarity to the ground-truth question-answer pairs.
Approach:	They propose an alternative approach to evaluate question-answer generation using Generative Interpretation (GI) GI outperforms existing evaluation methods in terms of human alignment .
Outcome:	The proposed approach outperforms existing evaluation methods in human alignment and shows comparable performance with GPT3.5, only with BART-large.

CharacterGPT: A Persona Reconstruction Framework for Role-Playing Agents (2025.naacl-industry)

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Jeiyoon Park, Chanjun Park, Heuiseok Lim

Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora (2025.acl-short)

Challenge:	Maintaining consistent character personas remains a significant challenge due to variability in information extraction.
Approach:	They propose a framework to dynamically reconstruct character personas through Character Persona Training.
Outcome:	The proposed framework is evaluated through Big Five personality evaluations and creative tasks, in which characters generate original narratives.

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Yungi Kim, Hyunsoo Ha, Sukyung Lee, Jihoo Kim, Seonghoon Yang, Chanjun Park

Explainable CED: A Dataset for Explainable Critical Error Detection in Machine Translation (2024.naacl-srw)

Challenge:	Existing methods to efficiently filter large web corpora require GPU resources.
Approach:	They propose an ensemble approach that leverages two contrasting KenLMs to filter large web corpora.
Outcome:	The proposed method significantly reduces noisy content while preserving high-quality content compared to the traditional KenLM training method.

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Dahyun Jung, Sugyeong Eo, Chanjun Park, Heuiseok Lim

Explainability & Interpretability in NLP

Challenge:	Existing studies of critical error detection lack content addressing the causes of catastrophic errors.
Approach:	They propose a dataset that introduces the attributes of error explanation and correction regarding critical errors.
Outcome:	The proposed dataset reduces time costs and mitigates human annotation bias.

MIRAGE: A Metric-Intensive Benchmark for Retrieval-Augmented Generation Evaluation (2025.findings-naacl)

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Chanhee Park, Hyeonseok Moon, Chanjun Park, Heuiseok Lim

Information Retrieval

Challenge:	Retrieval-Augmented Generation (RAG) systems are limited in their evaluation due to the intricate interplay between retrieval and generation components.
Approach:	They propose a Question Answering Question Answerer dataset specifically designed for RAG evaluation that integrates external, non-parametric knowledge retrieved by a retrieval pool of 37,800 entries.
Outcome:	The proposed dataset consists of 7,560 curated instances mapped to a retrieval pool of 37,800 entries, enabling an efficient evaluation of both retrieval and generation tasks.

Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing (2022.lrec-1)

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Hyeonseok Moon, Chanjun Park, Seolhwa Lee, Jaehyung Seo, Jungseob Lee, Sugyeong Eo, Heuiseok Lim

Text Generation

Challenge:	Automatic post-editing (APE) is a research field that aims to correct errors in translated sentences regardless of the utilized machine translation system.
Approach:	They propose a method for automatically generating APE data based on a noising scheme from a parallel corpus.
Outcome:	The proposed method shows that depending on the type of noise, the noising scheme-based APE data generation may lead to inferior performance.

Understanding LLM Development Through Longitudinal Study: Insights from the Open Ko-LLM Leaderboard (2025.naacl-industry)

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Chanjun Park, Hyeonwoo Kim

Challenge:	Existing studies on the Open Ko-LLM Leaderboard have been limited to five months . this limited analysis of the Open LLM Leaderboard provides a more comprehensive understanding of the progress in developing large language models.
Approach:	They conduct a longitudinal study over eleven months to address limitations of previous studies . they analyze 1,769 models over this period to provide a more comprehensive understanding .
Outcome:	The study extends observation period of the Open Ko-LLM Leaderboard to eleven months . primary questions are: What are the specific challenges in improving LLM performance?

Open Ko-LLM Leaderboard2: Bridging Foundational and Practical Evaluation for Korean LLMs (2025.naacl-industry)

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Hyeonwoo Kim, Dahyun Kim, Jihoo Kim, Sukyung Lee, Yungi Kim, Chanjun Park

Find the Intention of Instruction: Comprehensive Evaluation of Instruction Understanding for Large Language Models (2025.findings-naacl)

Challenge:	Open Ko-LLM Leaderboard has been instrumental in benchmarking Korean Large Language Models . however, the leaderboard has faced significant limitations over time due to its academic nature .
Approach:	They propose an improved version of the Open Ko-LLM Leaderboard to improve benchmarking . original benchmarks replaced with new tasks that align with real-world capabilities . four new native Korean benchmarks are introduced to better reflect distinct characteristics of Korean language .
Outcome:	The proposed framework improves the Open Ko-LLM Leaderboard2 benchmark suite.

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Hyeonseok Moon, Jaehyung Seo, Seungyoon Lee, Chanjun Park, Heuiseok Lim

FreeTalky: Don’t Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue (2022.lrec-1)

Challenge:	LLMs are prone to generate responses to instruction-formatted statements in an instinctive manner, rather than comprehending the underlying user intention within the given instructions.
Approach:	They propose to use an instruction-following capability benchmark to evaluate LLMs' instruction understanding capability.
Outcome:	The proposed benchmark analyzes the instruction understanding capability of large language models with four instruction candidates and a single candidate.

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Chanjun Park, Yoonna Jang, Seolhwa Lee, Sungjin Park, Heuiseok Lim

Natural Language Interfaces

Challenge:	FreeTalky is a deep learning-based foreign language learning platform for people who experience anxiety dealing with foreign languages.
Approach:	They propose a deep learning-based foreign language learning platform called FreeTalky . it employs a humanoid robot NAO and various deep learning models .
Outcome:	The proposed system provides personalized learning based on persona dialogue and grammar error correction, and also helps alleviate xenoglossophobia by replacing the real human in the conversation with a NAO robot, through human evaluation.

Translation of Multifaceted Data without Re-Training of Machine Translation Systems (2024.findings-emnlp)

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Hyeonseok Moon, Seungyoon Lee, SeongTae Hong, Seungjun Lee, Chanjun Park, Heuiseok Lim

Text Generation

Challenge:	a novel MT pipeline that considers the intra-data relation is proposed . previous MT systems have demonstrated relatively low performance, making them hardly utilized as another data source.
Approach:	They propose a new MT pipeline that considers the intra-data relation . they propose CS and IT to enhance the intra data relation based on a data point .
Outcome:	The proposed pipeline improves translation quality and training data compared with the existing approach . it yields better training data and better translation quality than previous approaches .

PicTalky: Augmentative and Alternative Communication for Language Developmental Disabilities (2022.aacl-demo)

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Chanjun Park, Yoonna Jang, Seolhwa Lee, Jaehyung Seo, Kisu Yang, Heuiseok Lim

Challenge:	Existing software packages are expensive and difficult to use, and only provide simple functions.
Approach:	They propose an AI-based AAC system called PicTalky that can improve communication skills for children with language disabilities.
Outcome:	The proposed system improves communication skills and language comprehension abilities for children with language disabilities.

LCIRC: A Recurrent Compression Approach for Efficient Long-form Context and Query Dependent Modeling in LLMs (2025.naacl-long)

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Sumin An, Junyoung Sung, Wonpyo Park, Chanjun Park, Paul Hongsuck Seo

Green & Sustainable NLP

Challenge:	Large language models excel in generating coherent and contextually rich outputs, but their capacity to handle long-form contexts is limited by fixed-length position embeddings.
Approach:	They propose a method that enables the efficient processing long-form sequences beyond the model’s length limit through recurrent compression without retraining the entire model.
Outcome:	The proposed method significantly improves LLM’s ability to manage extended contexts, making it well-suited for tasks that require both comprehensive context understanding and query relevance.

HAWK: Highlighting Entity-aware Knowledge for Alleviating Information Sparsity in Long Contexts (2025.findings-emnlp)

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Seonmin Koo, Jinsung Kim, Chanjun Park, Heuiseok Lim

Question Answering

Challenge:	a problem of information sparsity in QA tasks is causing fragmentation of textual data . highlighting entity-AWare Knowledge (HAWK) framework can be used to address this problem .
Approach:	a framework is proposed to highlight key information in a context and structuralize it in an entity-aware manner.
Outcome:	a proposed framework improves QA tasks with long contexts by highlighting key information in a context . the framework achieves a 27.6-point F1 score increase and an average win rate of 76.75% .

Mixture-of-Clustered-Experts: Advancing Expert Specialization and Generalization in Instruction Tuning (2025.emnlp-main)

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Sugyeong Eo, Jung Jun Lee, Chanjun Park, Heuiseok Lim

Text Clustering

Challenge:	A sparse Mixture-of-Experts architecture has emerged as a highly scalable solution for instruction tuning.
Approach:	They propose a mixture-of-Clustered-Experts (MoCE) architecture that allows expert specialization . they evaluate the mechanism on a set of benchmarks and show its superiority .
Outcome:	The proposed approach outperforms existing models and benchmarks on instruction tuning scenarios with significant input heterogeneity.

Leveraging Pre-existing Resources for Data-Efficient Counter-Narrative Generation in Korean (2024.lrec-main)

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Seungyoon Lee, Chanjun Park, DaHyun Jung, Hyeonseok Moon, Jaehyung Seo, Sugyeong Eo, Heuiseok Lim

From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems (2025.acl-srw)

Challenge:	Existing datasets and methods for detecting hate speech are limited by resource-intensive nature and only focus on the primary language.
Approach:	They propose a Korean Hate Speech Counter Punch (KHSCP) method that generates fact-based responses to hate speech in the Korean language and propose to use existing resources to overcome data scarcity.
Outcome:	The proposed method can overcome data scarcity in low-resource environments by leveraging existing resources.

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Youngjoon Jang, Seongtae Hong, Junyoung Son, Sungjin Park, Chanjun Park, Heuiseok Lim

Information Extraction & Text Mining

Challenge:	Retrieval-augmented generation (RAG) is a key framework in natural language processing . however, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents .
Approach:	They investigate how entity coreference affects document retrieval and generative performance in RAG-based systems.
Outcome:	The proposed model improves QA performance and retrieval relevance and contextual understanding.

sDPO: Don’t Use Your Data All at Once (2025.coling-industry)

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Dahyun Kim, Yungi Kim, Wonho Song, Hyeonwoo Kim, Yunsu Kim, Sanghoon Kim, Chanjun Park

Search if you don’t know! Knowledge-Augmented Korean Grammatical Error Correction with Large Language Models (2024.findings-emnlp)

Challenge:	Large language models (LLMs) are increasingly requiring precision and accuracy in alignment tuning.
Approach:	They propose a stepwise DPO technique that partitions available preference datasets incrementally rather than utilizing entire dataset simultaneously.
Outcome:	The proposed technique improves the accuracy of reference models and the overall performance of the final model.

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Seonmin Koo, Jinsung Kim, Chanjun Park, Heuiseok Lim