Papers by Kyomin Jung

Harmful Prompt Laundering: Jailbreaking LLMs with Abductive Styles and Symbolic Encoding (2025.emnlp-main)

Challenge:	Gender bias is a significant issue in machine translation, but most studies focus on debiasing bilingual models without consideration for multilingual systems.
Approach:	They propose a method which debiases bilingual models for unambiguous cases where there is a single correct translation.
Outcome:	The proposed method improves gender accuracy by a wide margin without hampering translation performance.

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Seongho Joo, Hyukhun Koh, Kyomin Jung

Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks (2020.lrec-1)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities but their misuse for harmful purposes remains a concern.
Approach:	They propose a jailbreaking technique that exploits weaknesses in LLMs' architecture . they propose abductive framing and symbolic encoding to bypass safeguards .
Outcome:	The proposed technique achieves over 95% attack success rate on GPT-series models and 70% across all targets.

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Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Kyomin Jung

Structured Data in NLP

Challenge:	Existing question-answering models do not require reasoning across sentences in the given context (passage).
Approach:	They propose a graph neural network that propagates information over sentences to understand information that cannot be inferred when considering sentences in isolation.
Outcome:	The proposed approach obtains the best performance compared to the widely used answer-selection models that do not consider the intersentential relationship.

Weakly Supervised Semantic Parsing with Execution-based Spurious Program Filtering (2023.emnlp-main)

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Kang-il Lee, Segwang Kim, Kyomin Jung

SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models (2025.findings-naacl)

Challenge:	Existing methods to train a semantic parser from weak supervision focus on exploiting similarities between examples based on domain-specific knowledge.
Approach:	They propose a domain-agnostic filtering mechanism based on program execution results to identify and filter out programs with significantly different semantics from the other programs.
Outcome:	The proposed method improves the performance of existing weakly-supervised parsers by incorporating a majority vote on the program search results.

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Jahyun Koo, Yerin Hwang, Yongil Kim, Taegwan Kang, Hyunkyung Bae, Kyomin Jung

Can LLMs Recognize Toxicity? A Structured Investigation Framework and Toxicity Metric (2024.findings-emnlp)

Challenge:	Knowledge Distillation (KD) has emerged as a popular method for compressing large language models due to high inference costs and memory requirements.
Approach:	They propose a method that integrates the teacher model during the student's sequence generation to reduce misguidance from the teacher.
Outcome:	Experiments on three model families and five instruction-following datasets show that SWITCH surpasses traditional methods, especially in the generation of long sequential data.

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Hyukhun Koh, Dohyung Kim, Minwoo Lee, Kyomin Jung

Challenge:	Existing toxicity metrics rely on encoder models trained on specific toxicity datasets, which are susceptible to out-of-distribution (OOD) problems and depend on the dataset’s definition of toxicity.
Approach:	They propose a robust metric grounded on LLMs to flexibly measure toxicity according to the given definition by analysing toxicity factors and intrinsic toxic attributes.
Outcome:	The proposed metric improves on conventional metrics by 12 points in the F1 score and shows that upstream toxicity significantly influences downstream metrics, suggesting that LLMs are unsuitable for toxicity evaluations within unverified factors.

AdvisorQA: Towards Helpful and Harmless Advice-seeking Question Answering with Collective Intelligence (2025.naacl-long)

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Minbeom Kim, Hwanhee Lee, Joonsuk Park, Hwaran Lee, Kyomin Jung

Question Answering

Challenge:	AdvisorQA aims to improve LLMs’ capability to offer advice for deeply subjective concerns, utilizing the LifeProTips Reddit forum.
Approach:	They propose a dataset to train LLMs' ability to offer advice for deeply subjective concerns, utilizing the LifeProTips Reddit forum.
Outcome:	The proposed model improves usefulness through automatic metric, GPT-4 and human evaluations, and expands independent evaluation axis to include harmlessness.

UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning (2021.acl-short)

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Hwanhee Lee, Seunghyun Yoon, Franck Dernoncourt, Trung Bui, Kyomin Jung

Return of EM: Entity-driven Answer Set Expansion for QA Evaluation (2025.coling-main)

Challenge:	BERTScore and other text generation metrics do not use reference captions to evaluate image captions.
Approach:	They propose a new metric which does not require reference captions to evaluate image captions . they train UMIC to discriminate negative captions via contrastive learning .
Outcome:	The proposed metric has higher correlation than previous metrics that require multiple references.

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Dongryeol Lee, Minwoo Lee, Kyungmin Min, Joonsuk Park, Kyomin Jung

Modality Alignment between Deep Representations for Effective Video-and-Language Learning (2022.lrec-1)

Challenge:	Recent studies show that using large language models (LLMs) is the most reliable method to evaluate QA models, but suffers from limited interpretability, high cost, and environmental harm.
Approach:	They propose to use soft exact match (EM) with entity-driven answer set expansion to expand gold answer set to include diverse surface forms.
Outcome:	The proposed method outperforms traditional evaluation methods while offering the benefits of high interpretability and reduced environmental harm.

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Hyeongu Yun, Yongil Kim, Kyomin Jung

Visual Data in NLP

Challenge:	Existing Video-and-Language models do not take into account the different characteristics of video and text representations.
Approach:	They propose a method that exploits Centered Kernel Alignment (CKA) to enhance cross-modality attention by combining multiple modalities.
Outcome:	The proposed method outperforms conventional multi-modal methods significantly on video QA tasks with +3.57% accuracy increment compared to the baseline in a popular benchmark dataset.

PR-MCS: Perturbation Robust Metric for MultiLingual Image Captioning (2023.findings-emnlp)

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Yongil Kim, Yerin Hwang, Hyeongu Yun, Seunghyun Yoon, Trung Bui, Kyomin Jung

Don’t Judge Code by Its Cover: Exploring Biases in LLM Judges for Code Evaluation (2026.findings-eacl)

Challenge:	Existing image captioning metrics are vulnerable to lexical perturbations, but they are not robust to such perturbations.
Approach:	They propose a perturbation-robust multilingual CLIPScore which is a reference-free image captioning metric for multiple languages.
Outcome:	The proposed metric outperforms baseline metrics in capturing lexical noise of all various perturbation types in all five languages while maintaining a strong correlation with human judgments.

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Jiwon Moon, Yerin Hwang, Dongryeol Lee, Taegwan Kang, Yongil Kim, Kyomin Jung

A Universal Avoidance Method for Diverse Multi-branch Generation (2026.findings-acl)

Challenge:	Large language models (LLMs) are increasingly used as evaluators for code evaluation tasks . however, whether they can handle superficial variations remains unclear .
Approach:	They define six types of potential biases in code evaluation and reveal their impact on LLM judges.
Outcome:	The proposed method can be used to evaluate semantically equivalent code with superficial variations without reference implementations.

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Kyeongman Park, Minha Jhang, Kyomin Jung

Challenge:	generative models still lack human-level creativity, especially in multi-branch diversity tasks.
Approach:	They propose a model-agnostic and computationally efficient generation strategy that penalizes similarity among previously generated outputs.
Outcome:	The proposed method achieves 1.9 times higher diversity, runs 4.4 times faster, and requires only 1/64 of the FLOPs compared to state-of-the-art methods.

FaithUn: Toward Faithful Forgetting in Language Models by Investigating the Interconnectedness of Knowledge (2025.emnlp-main)

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Nakyeong Yang, Minsung Kim, Seunghyun Yoon, Joongbo Shin, Kyomin Jung

Fooling the LVLM Judges: Visual Biases in LVLM-Based Evaluation (2025.emnlp-main)

Challenge:	Existing methods for unlearning undesirable knowledge have overlooked complexity and interconnectedness of knowledge, authors say . previous studies have neglected the complex nature of knowledge and neglected its internal dependencies.
Approach:	They propose a new concept called superficial unlearning to evaluate faithfulness of unlearning in knowledge QA settings.
Outcome:	The proposed method shows significant effectiveness in real-world knowledge QA settings.

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Yerin Hwang, Dongryeol Lee, Kyungmin Min, Taegwan Kang, Yongil Kim, Kyomin Jung

How Training Data Shapes the Use of Parametric and In-Context Knowledge in Language Models (2026.acl-long)

Challenge:	a recent study has found that large vision–language models are vulnerable to visual biases that inflate scores without altering semantic content.
Approach:	They propose a novel meta-evaluation benchmark that exhibits diverse score distributions.
Outcome:	The proposed model exhibits vulnerability across all domains, and combines multiple biases amplifies their effects, and pairwise evaluations are similarly susceptible.

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Minsung Kim, Dong-Kyum Kim, Jea Kwon, Nakyeong Yang, Kyomin Jung, Meeyoung Cha

KPQA: A Metric for Generative Question Answering Using Keyphrase Weights (2021.naacl-main)

Challenge:	Large language models leverage parametric and in-context knowledge in training . however, when these sources conflict, models arbitrate based on their internal confidence .
Approach:	They conduct controlled experiments using synthetic corpora to identify data properties that shape knowledge utilization.
Outcome:	The results show that the robust use of both knowledge sources is an emergent property . the results provide guidance for designing training data that supports the reliability of parametric and in-context knowledge in language models.

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Hwanhee Lee, Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Joongbo Shin, Kyomin Jung

Drift: Decoding-time Personalized Alignments with Implicit User Preferences (2025.findings-emnlp)

Challenge:	Existing n-gram similarity metrics fail to discriminate the incorrect answers due to the free-form of the answer.
Approach:	They propose a new metric that assigns different weights to each token via keyphrase prediction to judge the correctness of GenQA.
Outcome:	The proposed metric has a significantly higher correlation with human judgments than existing metrics in various datasets.

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Minbeom Kim, Kang-il Lee, Seongho Joo, Hwaran Lee, Thibaut Thonet, Kyomin Jung

MP2D: An Automated Topic Shift Dialogue Generation Framework Leveraging Knowledge Graphs (2024.emnlp-main)

Challenge:	Drift personalizes large language models at decoding time with implicit user preferences . Unlike traditional Reinforcement Learning from Human Feedback, Drift operates in a training-free manner .
Approach:	They propose a framework that personalizes large language models at decoding time with implicit user preferences.
Outcome:	The proposed framework personalizes large language models at decoding time with implicit user preferences.

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Yerin Hwang, Yongil Kim, Yunah Jang, Jeesoo Bang, Hyunkyung Bae, Kyomin Jung

Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination (2024.acl-long)

Challenge:	Existing methods to manage topic shifts within on-topic dialogues are limited in their ability to generate training datasets.
Approach:	They propose a data generation framework that automatically generates conversational question-answering datasets with natural topic transitions by leveraging relationships between entities in a knowledge graph.
Outcome:	The proposed framework generates conversational question-answering datasets with natural topic transitions and proves its effectiveness in generating dialogues with topic shifts.

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Nakyeong Yang, Taegwan Kang, Stanley Jungkyu Choi, Honglak Lee, Kyomin Jung

Critic-Guided Decoding for Controlled Text Generation (2023.findings-acl)

Challenge:	Existing methods to mitigate undesirable biases in instruction-following language models are not effective in accelerating instruction-based learning.
Approach:	They propose a method to eliminate bias neurons of language models in instruction-following settings by defining the bias neuron and prove its existence empirically.
Outcome:	The proposed method dramatically increases the task performance of language models under zero-shot instruction-following settings without losing the model’s knowledge.

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Minbeom Kim, Hwanhee Lee, Kang Min Yoo, Joonsuk Park, Hwaran Lee, Kyomin Jung

Dialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources (2023.emnlp-main)

Challenge:	Recent work has demonstrated reinforcement learning and weighted decoding as effective approaches to achieve a higher level of language control and quality with pros and cons.
Approach:	They propose a method that combines reinforcement learning and weighted decoding to train a critic from reward models.
Outcome:	The proposed method generates more coherent and well-controlled texts than previous methods on three controlled generation tasks, topic control, sentiment control, and detoxification.

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Yerin Hwang, Yongil Kim, Hyunkyung Bae, Hwanhee Lee, Jeesoo Bang, Kyomin Jung

A Character-Centric Creative Story Generation via Imagination (2025.findings-acl)

Challenge:	Existing dialog inpainting methods generate ConvQA datasets with low contextual relevance due to insufficient learning of question-answer alignment.
Approach:	They propose a dialog inpainting method that generates ConvQA datasets from documents . they propose re-ranking tasks and a framework that generate contextually relevant questions .
Outcome:	The proposed framework generates ConvQA datasets with high contextual relevance from textual sources.

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Kyeongman Park, Minbeom Kim, Kyomin Jung

VLind-Bench: Measuring Language Priors in Large Vision-Language Models (2025.findings-naacl)

Challenge:	Existing narrative generation models lack diversity and character depth, but they are inadequate for human creativity.
Approach:	They propose a novel story generation framework called CCI that leverages images to create stories that are diverse and creative in their themes and richer in content.
Outcome:	The proposed framework significantly improves various aspects of the stories’ creativity.

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Kang-il Lee, Minbeom Kim, Seunghyun Yoon, Minsung Kim, Dongryeol Lee, Hyukhun Koh, Kyomin Jung

Mitigating Hallucinations in Large Vision-Language Models via Summary-Guided Decoding (2025.findings-naacl)

Challenge:	Large Vision-Language Models suffer from a problem known as language prior . such language priors can lead to undesirable biases and hallucinations when dealing with images that are out of distribution.
Approach:	They propose a benchmark to measure the language priors of Large Vision-Language Models.
Outcome:	The proposed benchmark is the first specifically designed to measure the language priors, or blindness, of LVLMs.

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Kyungmin Min, Minbeom Kim, Kang-il Lee, Dongryeol Lee, Kyomin Jung

LifeTox: Unveiling Implicit Toxicity in Life Advice (2024.naacl-short)

Challenge:	Large Vision-Language Models (LVLMs) generate detailed and coherent responses from visual inputs but are prone to generate hallucinations due to an over-reliance on language priors.
Approach:	They propose a method that reduces the text context and controls only the image-related POS tokens to maintain text quality by reducing the text contextualization.
Outcome:	The proposed method achieves state-of-the-art performance on object hallucination benchmarks and achieves Pareto optimality among the existing methods.

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Minbeom Kim, Jahyun Koo, Hwanhee Lee, Joonsuk Park, Hwaran Lee, Kyomin Jung

Generating Diverse Hypotheses for Inductive Reasoning (2025.naacl-long)

Challenge:	Existing safety benchmarks and red teaming prompts fail to capture implicit toxicity in complex real-life advice-seeking scenarios.
Approach:	They propose a dataset designed for identifying implicit toxicity within advice-seeking scenarios.
Outcome:	The proposed dataset matches or surpasses the zero-shot performance of large language models in toxicity classification tasks.

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Kang-il Lee, Hyukhun Koh, Dongryeol Lee, Seunghyun Yoon, Minsung Kim, Kyomin Jung

Reasoning

Challenge:	Recent studies suggest that large language models (LLMs) can engage in inductive reasoning by sampling multiple hypotheses about the rules and selecting the one that best explains the observations.
Approach:	They propose to increase the temperature parameter to enhance diversity by sampling multiple hypotheses and selecting the one that best explains the observations.
Outcome:	The proposed method improves diversity while maintaining text quality while increasing temperature.

Public Data Assisted Differentially Private In-Context Learning (2025.findings-emnlp)

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Seongho Joo, Hyukhun Koh, Kyomin Jung

Persona Switch: Mixing Distinct Perspectives in Decoding Time (2026.findings-eacl)

Challenge:	In-context learning has shown remarkable performance across tasks without fine-tuning . however, recent studies have highlighted the risk of private data leakage through the prompt in ICL .
Approach:	They propose a private in-context learning algorithm that effectively balances privacy protection and model utility.
Outcome:	The proposed algorithm is robust against membership inference attacks and is robust to membership infertility attacks.

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Junseok Kim, Nakyeong Yang, Kyomin Jung

Task-specific Compression for Multi-task Language Models using Attribution-based Pruning (2023.findings-eacl)

Challenge:	Existing studies show that role-play prompting improves zero-shot reasoning, but these improvements are inconsistent across tasks and instances.
Approach:	They propose a method that dynamically combines the benefits of both prompting strategies.
Outcome:	The proposed method outperforms baselines and shows that output confidence is an important measure for selecting the more reliable output.

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Nakyeong Yang, Yunah Jang, Hwanhee Lee, Seohyeong Jeong, Kyomin Jung

Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning (2020.acl-main)

Challenge:	Existing compression methods for multi-task language models use large number of parameter parameters even when performing only a specific task.
Approach:	They propose a training-free compression method for multi-task language models using pruning method . they use an attribution method to determine which neurons are essential for performing a specific task .
Outcome:	The proposed method outperforms baseline pruning methods on six widely-used datasets.

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Joongbo Shin, Yoonhyung Lee, Seunghyun Yoon, Kyomin Jung

Reliability-Aware Adaptive Self-Consistency for Efficient Sampling in LLM Reasoning (2026.findings-acl)

Challenge:	Existing deep bidirectional language models are limited by repetitive inferences on unsupervised tasks for the computation of contextual language representations.
Approach:	They propose a deep bidirectional language model called a Transformer-based Text Autoencoder (T-TA) it computes contextual language representations without repetition and shows competitive or even better accuracies than BERT .
Outcome:	The proposed model performs six times faster on a reranking task and twelve times faster in a semantic similarity task.

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Junseok Kim, Nakyeong Yang, Kyungmin Min, Kyomin Jung

Asking Clarification Questions to Handle Ambiguity in Open-Domain QA (2023.findings-emnlp)

Challenge:	Self-consistency improves reasoning reliability but incurs substantial inference cost . Adaptive self-consistent methods rely on count-based stopping rules that treat all responses equally .
Approach:	They propose a method that reframs adaptive sampling from response counting to evidence sufficiency by leveraging response-level confidence.
Outcome:	The proposed method reduces inference cost by up to 70% while preserving accuracy on GSM8K.

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Dongryeol Lee, Segwang Kim, Minwoo Lee, Hwanhee Lee, Joonsuk Park, Sang-Woo Lee, Kyomin Jung

Avoidance Decoding for Diverse Multi-Branch Story Generation (2025.emnlp-main)

Challenge:	Ambiguous questions persist in open-domain question answering because formulating a precise question with a unique answer is often challenging.
Approach:	They propose to ask a clarification question where the user’s response will help identify the interpretation that best aligns with the user's intention.
Outcome:	The proposed approach achieves F1 of 61.3, 25.1, and 40.5 on the three tasks, demonstrating the need for further improvements while providing competitive baselines for future work.

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Kyeongman Park, Nakyeong Yang, Kyomin Jung

Text Generation

Challenge:	Existing studies have attempted to increase the diversity of generated texts through decoding-time methods.
Approach:	They propose a decoding strategy that penalizes similarity to previously generated logits to encourage more diverse multi-branch stories.
Outcome:	The proposed method achieves up to 2.6 times higher output diversity and reduces repetition by an average of 30% compared to strong baselines, while effectively mitigating text degeneration.

BREAK: Breaking the Dialogue State Tracking Barrier with Beam Search and Re-ranking (2023.acl-long)

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Seungpil Won, Heeyoung Kwak, Joongbo Shin, Janghoon Han, Kyomin Jung

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for dialogue state tracking still have a JGA of 60% on MultiWOZ 2.1 . break framework provides a simple yet effective way to generate dialogue state candidates .
Approach:	They propose a framework that generates k-best dialogue state candidates with beam search and re-ranks them to select the correct dialogue state.
Outcome:	The proposed framework pushes the joint goal accuracy to 80-90% on MultiWOZ 2.1-2.4.

Are LLM-Judges Robust to Expressions of Uncertainty? Investigating the effect of Epistemic Markers on LLM-based Evaluation (2025.naacl-long)

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Dongryeol Lee, Yerin Hwang, Yongil Kim, Joonsuk Park, Kyomin Jung

ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflection (2025.emnlp-main)

Challenge:	Existing efforts to train large language models to generate outputs containing epistemic markers have been largely overlooked.
Approach:	They propose a benchmark to assess the robustness of LLM-judges to epistemic markers.
Outcome:	EMBER benchmarks show that LLM-judges lack robustness in presence of epistemic markers . EMber QA (2,000 instances) and IF (2823 instances) are used to evaluate outputs containing epistemological markers.

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Jeonghye Kim, Sojeong Rhee, Minbeom Kim, Dohyung Kim, Sangmook Lee, Youngchul Sung, Kyomin Jung

Reasoning

Challenge:	Recent advances in LLMs have significantly enhanced their reasoning capabilities, enabling LLM-based agents to perform complex multi-step decision making beyond static problem solving.
Approach:	They propose a novel reasoning backbone that shifts reasoning from merely planning next actions to continuously reflecting on the agent’s state relative to its goal.
Outcome:	The proposed model outperforms ReAct by 27.7% on average, achieving a 93.3% success rate in ALFWorld.

IterCQR: Iterative Conversational Query Reformulation with Retrieval Guidance (2024.naacl-long)

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Yunah Jang, Kang-il Lee, Hyunkyung Bae, Hwanhee Lee, Kyomin Jung

Evaluating Visual Narrative Coherence in Story Visualization via Diversified Storylines (2026.acl-long)

Challenge:	Existing methods for conversational query reformulation depend on human annotations.
Approach:	They propose a method that reformulates context-dependent conversational queries without relying on human rewrites.
Outcome:	The proposed method shows state-of-the-art performance on two widely-used datasets.

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Minha Jhang, Kyeongman Park, Hyukhun Koh, Kyomin Jung

Visual Data in NLP

Challenge:	Existing evaluation metrics and datasets often neglect visual continuity and narrative diversity.
Approach:	They propose a visual context-aware metric for story visualization that uses large vision-language models to jointly assess caption fidelity and inter-image consistency.
Outcome:	The proposed framework achieves a Spearman’s correlation comparable to human agreement on two benchmarks and blends diverse and controlled narrative elements at adjustable ratios, producing challenging evaluation sets.

DPP-TTS: Diversifying prosodic features of speech via determinantal point processes (2023.emnlp-main)

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Seongho Joo, Hyukhun Koh, Kyomin Jung

Can You Trick the Grader? Adversarial Persuasion of LLM Judges (2025.findings-emnlp)

Challenge:	Recent advances in deep generative models have succeeded in synthesizing human-like speech.
Approach:	They propose a text-to-speech model with a prosody diversifying module that considers perceptual diversity in each sample and among multiple samples.
Outcome:	The proposed model generates speech samples with more diversified prosody than baselines in the side-by-side comparison test considering the naturalness of speech at the same time.

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Yerin Hwang, Dongryeol Lee, Taegwan Kang, Yongil Kim, Kyomin Jung

Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking (2022.findings-naacl)

Challenge:	Large language models (LLMs) are increasingly used as automated evaluators in practical settings .
Approach:	a study by the university of california reveals that persuasive language can bias large language models when scoring mathematical reasoning tasks.
Outcome:	The proposed model can bias judges when scoring mathematical reasoning tasks . Consistency causes the most severe distortion, with Consistencies leading to 8% distortion .

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Hwanhee Lee, Kang Min Yoo, Joonsuk Park, Hwaran Lee, Kyomin Jung

Information Extraction & Text Mining

Challenge:	Abstractive summarization systems generate paraphrases, but they often contain information inconsistent with the source text.
Approach:	They propose to generate factually inconsistent summaries using source texts and reference summary with key information masked to train a factual consistency classifier.
Outcome:	The proposed method outperforms existing models and shows a competitive correlation with human judgments.

Learning to Rank Question-Answer Pairs Using Hierarchical Recurrent Encoder with Latent Topic Clustering (N18-1)

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Seunghyun Yoon, Joongbo Shin, Kyomin Jung

LLMs can be easily Confused by Instructional Distractions (2025.acl-long)

Challenge:	Existing models for sentence pair ranking are based on hierarchical recurrent neural network and latent topic clustering module.
Approach:	They propose a hierarchical recurrent neural network and latent topic clustering module to adapt a recursive hierarchic neural network to rank candidate answers.
Outcome:	The proposed model shows small performance degradations in longer text comprehension compared to current models which suffer from it.

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Yerin Hwang, Yongil Kim, Jahyun Koo, Taegwan Kang, Hyunkyung Bae, Kyomin Jung

Reasoning

Challenge:	Large language models (LLMs) show exceptional skill in instruction following tasks, but can become vulnerable when they are required to disregard instructions.
Approach:	They propose a benchmark to assess LLMs' performance under instructional distraction.
Outcome:	The proposed benchmark categorizes real-world instances of instructional distraction and evaluates LLMs across four instruction tasks: proofreading, rewriting, translation, and style transfer—alongside five input tasks: reasoning, code generation, mathematical reasoning, bias detection, and question answering.

Kosmic: Korean Text Similarity Metric Reflecting Honorific Distinctions (2024.lrec-main)

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Yerin Hwang, Yongil Kim, Hyunkyung Bae, Jeesoo Bang, Hwanhee Lee, Kyomin Jung

Paraphrasing

Challenge:	Existing methods for text similarity measurement focus on the semantic dimension, neglecting the unique linguistic attributes found in languages like Korean.
Approach:	They propose a Korean text-similarity metric that encompasses the semantic and tonal facets of a given text pair.
Outcome:	The proposed method outperforms existing methods in Korean and other languages . it identifies which methods preserve semantics and tone while preserving similarity .

Fine-grained Gender Control in Machine Translation with Large Language Models (2024.naacl-long)

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Minwoo Lee, Hyukhun Koh, Minsung Kim, Kyomin Jung

Conditional [MASK] Discrete Diffusion Language Model (2025.emnlp-main)

Challenge:	Existing work on controlled translation has only considered a simplified setup of one target gender for input.
Approach:	They propose a Gender-of-Entity prompting method for machine translation that takes the gender of the ambiguous entity as additional input and propose to use it to translate with correct gender inflections.
Outcome:	The proposed method instructs the model with fine-grained entity-level gender information to translate with correct gender inflections.

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Hyukhun Koh, Minha Jhang, Dohyung Kim, Sangmook Lee, Kyomin Jung

Injecting Comparison Skills in Task-Oriented Dialogue Systems for Database Search Results Disambiguation (2023.findings-acl)

Challenge:	Auto-regressive models excel in natural language processing but struggle to generate diverse text and lack controllability.
Approach:	They propose entropy-adaptive Gibbs sampling and entropic-based noise scheduling to counterbalance each model’s shortcomings.
Outcome:	The proposed framework outperforms baseline models and achieves the best quality-diversity tradeoff, demonstrating its effectiveness in non-autoregressive text generation.

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Yongil Kim, Yerin Hwang, Joongbo Shin, Hyunkyung Bae, Kyomin Jung

QACE: Asking Questions to Evaluate an Image Caption (2021.findings-emnlp)

Challenge:	Existing task-oriented dialogue systems handle ambiguity in database search results by randomly selecting one or few results and presenting their names to the user.
Approach:	They propose a task that compares properties of multiple database search results . they use a dataset to collect high-quality dialogue data and an augmented version of the SGD dataset .
Outcome:	The proposed task compares properties of two entities in a trade-off form based on user preferences . the proposed dataset and code will be publicized .

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Hwanhee Lee, Thomas Scialom, Seunghyun Yoon, Franck Dernoncourt, Kyomin Jung

Rethinking Post-Unlearning Behavior of Large Vision-Language Models (2026.findings-acl)

Challenge:	Existing metric for image captioning evaluation is based on n-gram similarity metrics but these fail to capture semantic errors in captions.
Approach:	They propose a new metric based on Question Answering for Caption Evaluation to evaluate image captioning based upon Question Generation and Question Answers systems.
Outcome:	The proposed metric is multi-modal, reference-less and explainable.

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Minsung Kim, Nakyeong Yang, Kyomin Jung