Papers by Seo Park

Two Examples are Better than One: Context Regularization for Gradient-based Prompt Tuning (2023.findings-acl)

Challenge:	Large language models (LLMs) enable zero-shot approaches in open domain question answering (ODQA), yet with limited advancements as the reader is compared to the retriever.
Approach:	They propose to use a distraction-aware answer selection framework to mitigate the impact of irrelevant documents in the retrieved set and the overconfidence of the generated answers to enhance the performance of zero-shot readers.
Outcome:	The proposed approach handles distraction across diverse scenarios, enhancing the performance of zero-shot readers.

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Hyeonmin Ha, Soyoung Jung, Jinsol Park, Minjoon Seo, Seung-won Hwang, Byung-Gon Chun

A Dog Is Passing Over The Jet? A Text-Generation Dataset for Korean Commonsense Reasoning and Evaluation (2022.findings-naacl)

Challenge:	Prompting has gained tremendous attention as an efficient method for the adaptation of large-scale language models.
Approach:	They propose a regularization method that guides a prompt to produce a task context properly.
Outcome:	The proposed method improves prediction performance in a zero-shot in-context learning setting without demonstration examples for in-constitu learning.

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Jaehyung Seo, Seounghoon Lee, Chanjun Park, Yoonna Jang, Hyeonseok Moon, Sugyeong Eo, Seonmin Koo, Heuiseok Lim

Reasoning

Challenge:	Korean pretrained language models struggle to generate short sentences with a given condition based on compositionality and commonsense reasoning.
Approach:	They propose a Korean text-generation dataset for Korean generative commonsense reasoning and language model evaluation using a semi-automatic dataset construction approach.
Outcome:	The proposed dataset is available at http://aihub.or.kr/opendata/korea-university.

Detecting Critical Errors Considering Cross-Cultural Factors in English-Korean Translation (2024.lrec-main)

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Sugyeong Eo, Jungwoo Lim, Chanjun Park, DaHyun Jung, Seonmin Koo, Hyeonseok Moon, Jaehyung Seo, Heuiseok Lim

Machine Translation

Challenge:	Recent machine translation systems overcome language barriers for a wide range of users, yet they carry the risk of catastrophic meaning deviations.
Approach:	They introduce a culture-aware "Politeness" type for detecting critical translation errors . they also provide multiclass labels for critical error detection and critical error type classification .
Outcome:	Empirical results show that the proposed method outperforms baselines in both tasks.

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

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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.

Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding (2025.findings-naacl)

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Sukmin Cho, Sangjin Choi, Taeho Hwang, Jeongyeon Seo, Soyeong Jeong, Huije Lee, Hoyun Song, Jong C. Park, Youngjin Kwon

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

Challenge:	Existing methods for drafting and verifying tokens require significant fine-tuning or have inconsistent performance across tasks.
Approach:	They propose a lossless drafting approach that organizes various token sources into multiple databases in a hierarchical framework based on temporal locality.
Outcome:	The proposed method outperforms existing database drafting methods on Spec-Bench using 7B and 13B parameters.

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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.

Hierarchical Deconstruction of LLM Reasoning: A Graph-Based Framework for Analyzing Knowledge Utilization (2024.emnlp-main)

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Miyoung Ko, Sue Park, Joonsuk Park, Minjoon Seo

Korean Bio-Medical Corpus (KBMC) for Medical Named Entity Recognition (2024.lrec-main)

Challenge:	Despite advances in large language models, how they use their knowledge for reasoning is not yet well understood.
Approach:	They propose a method that deconstructs complex real-world questions into a graph . they quantify forward discrepancy, a discrepany in LLM performance on simpler sub-problems .
Outcome:	The proposed method shows that smaller models exhibit more discrepancies than larger models . it also shows that guiding models from simpler to complex questions improves performance .

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Sungjoo Byun, Jiseung Hong, Sumin Park, Dongjun Jang, Jean Seo, Minseok Kim, Chaeyoung Oh, Hyopil Shin

Named Entity Recognition

Challenge:	Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP) yet, there is no open-source medical NER dataset specifically for Korean.
Approach:	They used ChatGPT to construct an open-source Korean NER dataset . they found 20% increase in medical NER performance compared to general Korean ner datasets.
Outcome:	The KBMC dataset shows an impressive 20% increase in medical NER performance compared to models trained on general Korean NER datasets.

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

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

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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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%.

Can Large Language Models Keep Up? Benchmarking Online Adaptation to Continual Knowledge Streams (2026.acl-long)

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Jiyeon Kim, Hyunji Lee, Dylan Zhou, Sue Hyun Park, Seunghyun Yoon, Trung Bui, Franck Dernoncourt, Sungmin Cha, Minjoon Seo

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

Challenge:	Existing models and agentic memory systems fail to adapt robustly to OAKS, demonstrating delays in state-tracking and susceptibility to distraction within streaming environments.
Approach:	They propose a benchmark to evaluate models' ability to adapt to changing knowledge over streaming . they use two datasets to analyze how facts evolve over time .
Outcome:	The proposed benchmark evaluates models in an online adaptation setting over streaming, continually updating knowledge.

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

SuperST: Superficial Self-Training for Few-Shot Text Classification (2024.lrec-main)

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.

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Ju-Hyoung Lee, Joonghyuk Hahn, Hyeon-Tae Seo, Jiho Park, Yo-Sub Han

Information Extraction & Text Mining

Challenge:	In few-shot text classification, self-training relies on pseudo-labels to expand data, which has shown success, but can accumulate errors due to noisy pseudo-labeled data.
Approach:	They propose a method to mitigate noise in noisy pseudo-labeled data by applying superficial learning to noisy data and fine-tuning to less noisy data.
Outcome:	The proposed framework improves the classifier accuracy for few-shot text classification by 18.5% at most and 8% in average, compared with the state-of-the-art SSL baselines.

Typos that Broke the RAG’s Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations (2024.findings-emnlp)

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Sukmin Cho, Soyeong Jeong, Jeongyeon Seo, Taeho Hwang, Jong Park

Robustness in NLP

Challenge:	Existing studies on the robustness of Large Language Models (LLMs) overlook the interconnected relationships between RAG components or the potential threats prevalent in real-world databases, such as minor textual errors.
Approach:	They propose a novel attack method that exploits vulnerabilities in RAG components and tests its robustness against noisy documents.
Outcome:	The proposed method devastates the performance of each component and their synergy, and significantly devases the performance.

MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data (2023.findings-eacl)

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Sung-Min Lee, Eunhwan Park, Daeryong Seo, Donghyeon Jeon, Inho Kang, Seung-Hoon Na

What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers (2021.emnlp-main)

Challenge:	Experimental results show that Transformer-based questions have a "long" hybrid sequence over tabular and textual elements, causing long-range reasoning problems.
Approach:	They propose a moving average-equipped fusion-in-decoder to handle long-range reasoning problems . they use FiD and EMA to combine different levels of reasoning .
Outcome:	Experimental results show that the proposed model increases exact matching and F1 by 1.1 and 1.7 on the blind test set.

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Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Jeon Dong Hyeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, Heungsub Lee, Minyoung Jeong, Sungjae Lee, Minsub Kim, Suk Hyun Ko, Seokhun Kim, Taeyong Park, Jinuk Kim, Soyoung Kang, Na-Hyeon Ryu, Kang Min Yoo, Minsuk Chang, Soobin Suh, Sookyo In, Jinseong Park, Kyungduk Kim, Hiun Kim, Jisu Jeong, Yong Goo Yeo, Donghoon Ham, Dongju Park, Min Young Lee, Jaewook Kang, Inho Kang, Jung-Woo Ha, Woomyoung Park, Nako Sung

Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis (2021.acl-short)

Challenge:	GPT-3 has been used to train large-scale language models on hundreds of billion scale data.
Approach:	They propose a Korean variant of GPT-3 that uses Korean tokens to train in-context models.
Outcome:	The proposed method shows state-of-the-art zero-shot and few-shot learning on downstream tasks in Korean.

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Shinhyeok Oh, Dongyub Lee, Taesun Whang, IlNam Park, Seo Gaeun, EungGyun Kim, Harksoo Kim

Sentiment Analysis

Challenge:	Existing methods for aspect-based sentiment analysis (ABSA) consider relationships implicitly among subtasks at the word level.
Approach:	They propose a deep contextualized relation-aware network that allows interactive relations among subtasks . they propose self-supervised strategies that deal with multiple aspects .
Outcome:	The proposed method outperforms state-of-the-art methods on three widely used benchmarks.

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

Responsible & Trustworthy NLP

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.

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

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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.

Ultra-High Dimensional Sparse Representations with Binarization for Efficient Text Retrieval (2021.emnlp-main)

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Kyoung-Rok Jang, Junmo Kang, Giwon Hong, Sung-Hyon Myaeng, Joohee Park, Taewon Yoon, Heecheol Seo

Representation Learning

Challenge:	Recent approaches to information retrieval (IR) and natural language processing (NLP) use contextual language models, which can improve both synonymy and polysemy problems associated with words.
Approach:	They propose an ultra-high dimensional representation scheme equipped with directly controllable sparsity and a bucketing method where embeddings from multiple layers of BERT are selected/merged to represent diverse linguistic aspects.
Outcome:	The proposed representation scheme outperforms sparse models with MS MARCO and TREC CAR, and shows that it is highly efficient for storage and search.

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

Attend What You Need: Motion-Appearance Synergistic Networks for Video Question Answering (2021.acl-long)

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.

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Ahjeong Seo, Gi-Cheon Kang, Joonhan Park, Byoung-Tak Zhang

Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation (2024.findings-acl)

Challenge:	Recent advances in natural language processing and computer vision have made significant progress in artificial intelligence (AI).
Approach:	They propose Motion-Appearance Synergistic Networks which embed cross-modal features grounded on motion and appearance information and selectively utilize them depending on the question’s intentions.
Outcome:	The proposed network achieves state-of-the-art on the TGIF-QA and MSVD-QA datasets and qualitatively analyzes the results.

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Seongyun Lee, Seungone Kim, Sue Park, Geewook Kim, Minjoon Seo

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

Challenge:	Existing metrics for long-form text outputs are prone to biases and scaling up is expensive.
Approach:	They propose to evaluate VLMs with VLM feedback dataset . they use 15K customized score rubrics to train Prometheus-Vision .
Outcome:	The proposed model shows highest correlation with human evaluators and GPT-4V among open-source models.

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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.

Ranking-Enhanced Unsupervised Sentence Representation Learning (2023.acl-long)

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Yeon Seonwoo, Guoyin Wang, Changmin Seo, Sajal Choudhary, Jiwei Li, Xiang Li, Puyang Xu, Sunghyun Park, Alice Oh

Spatial Dependency Parsing for Semi-Structured Document Information Extraction (2021.findings-acl)

Challenge:	Unsupervised sentence representation learning has progressed through contrastive learning and data augmentation methods such as dropout masking.
Approach:	They propose a novel unsupervised sentence encoder, RankEncoder, which predicts the semantic vector of an input sentence by leveraging its relationship with other sentences in an external corpus.
Outcome:	The proposed unsupervised sentence encoder achieves 80.07% Spearman’s correlation, a 1.1% improvement over the previous state-of-the-art system.

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Wonseok Hwang, Jinyeong Yim, Seunghyun Park, Sohee Yang, Minjoon Seo

Syntactic Text Processing

Challenge:	Information extraction (IE) for semistructured document images is often considered as a sequence tagging problem . however, such a setup cannot handle complex spatial relationships and is not suitable for highly structured information.
Approach:	They propose a spatial dependency parsing problem that models complex spatial relationships . they evaluate it on receipts, name cards, forms, and invoices and compare it to other methods .
Outcome:	The proposed parser achieves similar or better performance on various kinds of documents compared to baselines including BERT-based IOB taggger.

ClaimDiff: Comparing and Contrasting Claims on Contentious Issues (2023.findings-acl)

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Miyoung Ko, Ingyu Seong, Hwaran Lee, Joonsuk Park, Minsuk Chang, Minjoon Seo

Reasoning

Challenge:	Using fact verification tasks, however, can not detect subtle differences in factually consistent claims, which might bias the readers.
Approach:	They propose a novel dataset that primarily focuses on comparing the nuance between claim pairs.
Outcome:	The proposed dataset shows that human-labeled 2,941 claim pairs are weaker than baselines, showing a 19% absolute gap with the baselines.

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.

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

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

Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker (2023.findings-acl)

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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Sukmin Cho, Soyeong Jeong, Jeong yeon Seo, Jong Park

The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models (2025.naacl-long)

Challenge:	Existing studies suggest re-rankers by fine-tuning pre-trained language models . however, manual search for discrete prompts is expensive and sub-optimal in transferability .
Approach:	They propose a discrete prompt optimization method that guides the generated texts toward optimal prompts . they propose to use large-scale language models as a zero-shot re-ranker .
Outcome:	The proposed method improves the performance of the re-ranker against baselines and human prompts.

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Seungone Kim, Juyoung Suk, Ji Yong Cho, Shayne Longpre, Chaeeun Kim, Dongkeun Yoon, Guijin Son, Yejin Cho, Sheikh Shafayat, Jinheon Baek, Sue Hyun Park, Hyeonbin Hwang, Jinkyung Jo, Hyowon Cho, Haebin Shin, Seongyun Lee, Hanseok Oh, Noah Lee, Namgyu Ho, Se June Joo, Miyoung Ko, Yoonjoo Lee, Hyungjoo Chae, Jamin Shin, Joel Jang, Seonghyeon Ye, Bill Yuchen Lin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo

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

Challenge:	a recent study evaluated language models using abstract evaluation criteria that lack the flexibility and granularity of human assessment.
Approach:	They propose a benchmark to evaluate nine distinct language models' capabilities . they use instance-specific evaluation criteria to mirror human evaluation .
Outcome:	The proposed benchmark evaluates nine distinct capabilities of language models across 77 tasks.

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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.

Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision (2024.naacl-long)

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Seongyun Lee, Sue Park, Yongrae Jo, Minjoon Seo

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

Challenge:	Recent studies have conjectured that multimodal hallucination is due to the vision encoder failing to ground on the image properly.
Approach:	They propose a multimodal self-feedback guided revision model that leverages visual cues to generate feedback to its initial response based on the visual information provided by the vision encoder.
Outcome:	The proposed model reduces multimodal hallucination and outperforms previous models on MMHal-Bench, POPE, and GAVIE.

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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.

Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation (2023.acl-long)

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Soyoung Yoon, Sungjoon Park, Gyuwan Kim, Junhee Cho, Kihyo Park, Gyu Tae Kim, Minjoon Seo, Alice Oh

Syntactic Text Processing

Challenge:	Despite the growing number of Korean learners, little research has been conducted on Korean grammatical error correction (GEC) despite the difficulties of the Korean language, there is no evaluation benchmark for Korean GEC.
Approach:	They propose to use Korean grammar error correction datasets to train a machine learning model that can automatically annotate Korean errors from parallel corpora.
Outcome:	The proposed model outperforms the currently used statistical Korean GEC system on a wider range of error types.

Multi-Task Knowledge Distillation with Embedding Constraints for Scholarly Keyphrase Boundary Classification (2023.emnlp-main)

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Seo Park, Cornelia Caragea

ReSCORE: Label-free Iterative Retriever Training for Multi-hop Question Answering with Relevance-Consistency Supervision (2025.acl-long)

Challenge:	Scholarly keyphrase boundary classification is the task of identifying highly summative phrases from scientific papers and classifying them into a set of predefined classes.
Approach:	They propose a constraint which enforces the teachers and student similarity in the embedding space.
Outcome:	The proposed constraint outperforms previous studies and strong baselines on three datasets of scientific documents.

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Dosung Lee, Wonjun Oh, Boyoung Kim, Minyoung Kim, Joonsuk Park, Paul Hongsuck Seo

Natural Language Interfaces

Challenge:	Multi-hop question answering requires reasoning across multiple documents to answer complex questions.
Approach:	They propose a method for training dense retrievers for multi-hop question answering . they leverage large language models to measure document-question relevance with answer consistency . their results lead to state-of-the-art Exact Match and F1 scores for MHQA .
Outcome:	Evaluated on three MHQA benchmarks, the proposed method improves retrieval performance . it leads to state-of-the-art Exact Match and F1 scores for the proposed technique .

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

Responsible & Trustworthy NLP

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.

HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering (2026.acl-long)

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Joongmin Shin, Gyuho Shim, Jeongbae Park, Jaehyung Seo, Heuiseok Lim