Papers by Jinyoung Kim

When Should Dense Retrievers Be Updated in Evolving Corpora? Detecting Out-of-Distribution Corpora Using GradNormIR (2025.findings-acl)

Challenge:	Memorizing and utilizing speakers’ personas is a common practice for response generation in long-term conversations, yet human-authored datasets often provide uninformative persona sentences that hinder response quality.
Approach:	They propose a framework that leverages commonsense-based persona expansion to address such issues in long-term conversations.
Outcome:	The proposed framework facilitates better response generation via human-like persona refinement.

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Dayoon Ko, Jinyoung Kim, Sohyeon Kim, Jinhyuk Kim, Jaehoon Lee, Seonghak Song, Minyoung Lee, Gunhee Kim

BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets (2022.emnlp-main)

Challenge:	Dense retrievers encode text into embeddings to retrieve relevant documents . however, real-world corpora evolve, resulting in degraded retrieval performance . identifying when a dense retriever requires an update is critical for robust retrieval systems .
Approach:	They propose a task of predicting whether a corpus is out-of-distribution (OOD) relative to a dense retriever before indexing.
Outcome:	The proposed method detects whether a corpus is out-of-distribution (OOD) relative to a dense retriever before indexing.

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Minju Kim, Chaehyeong Kim, Yong Ho Song, Seung-won Hwang, Jinyoung Yeo

Natural Language Interfaces

Challenge:	a number of largescale datasets targeting a specific conversational skill have recently become available.
Approach:	They propose a framework where multiple agents grounded to specific skills participate in a conversation to automatically annotate multi-skill dialogues.
Outcome:	The proposed framework can be used to build open-domain chatbots with diverse communicative skills.

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

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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.

LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study (2025.acl-long)

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Dongil Yang, Minjin Kim, Sunghwan Kim, Beong-woo Kwak, Minjun Park, Jinseok Hong, Woontack Woo, Jinyoung Yeo

Multimodality

Challenge:	Large language models (LLMs) have demonstrated impressive progress in various text-based tasks, such as question-answering and content generation.
Approach:	They propose a benchmark to evaluate Large Language Models’ ability to understand scene graphs and generate them from textual narratives.
Outcome:	The proposed model performs well on scene graph understanding but struggles with scene graph generation, particularly for complex narratives.

One Missing Piece for Open-Source Reasoning Models: A Dataset to Mitigate Cold-Starting Short CoT LLMs in RL (2025.acl-industry)

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Hyungjoo Chae, Dongjin Kang, Jihyuk Kim, Beong-woo Kwak, Sunghyun Park, Haeju Park, Jinyoung Yeo, Moontae Lee, Kyungjae Lee

Reasoning

Challenge:	Existing large reasoning models are limited by their closed nature and high API costs and safety issues.
Approach:	They propose to build a long CoT dataset with existing short CoT LLMs that are not trained for inference-time scaling.
Outcome:	The proposed model achieves quality comparable to—or slightly below—R1 and is able to think longer and provide control over the thought budget to better manage the overthinking problem.

SelFusion: Self-distillation for Diffusion Language Models (2026.acl-long)

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Hyeongsoo Lim, Jinyoung Kim, Eun Seo Seo, Min Ho Jang, Ji Won Yoon

Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset (2024.findings-acl)

Challenge:	Existing knowledge distillation methods for autoregressive large language models (LLMs) are not effective for reducing generation quality, but they can be useful for real-time applications.
Approach:	They propose a self-distillation framework that allows for effective KD without external teacher . they propose to use two modes of knowledge distillation to determine distillation direction .
Outcome:	The proposed framework outperforms existing methods with external teachers on instruction-following tasks.

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Minjin Kim, Minju Kim, Hana Kim, Beong-woo Kwak, SeongKu Kang, Youngjae Yu, Jinyoung Yeo, Dongha Lee

Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning (2022.naacl-main)

Challenge:	Existing datasets for conversational recommender systems lack specific user preferences and explanations for recommendations . current datasets lack specific preferences, hindering high-quality recommendations despite advances in large language models .
Approach:	They propose to synthesize a conversational recommendation dataset with persona- and knowledge-augmented LLM simulators to address these challenges.
Outcome:	The proposed dataset outperforms baselines in human and automatic evaluations.

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Yu Jin Kim, Beong-woo Kwak, Youngwook Kim, Reinald Kim Amplayo, Seung-won Hwang, Jinyoung Yeo

DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG (2024.emnlp-main)

Challenge:	Currently, commonsense reasoning systems are limited by expensive data annotations and overfitting to a specific benchmark.
Approach:	They propose to transform a commonsense knowledge graph into synthetic QA-form samples for model training.
Outcome:	The proposed framework improves performance with multiple commonsense KGs on five commonsensense reasoning benchmarks.

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Jinyoung Kim, Dayoon Ko, Gunhee Kim

Knowledge Representation

Challenge:	Existing entity linking models struggle to link new expressions to entities in the dynamic nature of human language.
Approach:	They propose a task to resolve emerging mentions to dynamic entities and a benchmark to evaluate their model's adaptability to new expressions.
Outcome:	The proposed method outperforms baselines on QA task with resolved mentions and improves retrieval-augmented generation performance.

Generative Subgraph Retrieval for Knowledge Graph–Grounded Dialog Generation (2024.emnlp-main)

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Jinyoung Park, Minseok Joo, Joo-Kyung Kim, Hyunwoo Kim

Learning Co-Speech Gesture for Multimodal Aphasia Type Detection (2023.emnlp-main)

Challenge:	Existing methods for knowledge graph–grounded dialog generation fail to leverage the rich knowledge of pretrained language models.
Approach:	They propose a method for dialog generation that integrates dialog history with a knowledge graph.
Outcome:	The proposed method achieves state-of-the-art in knowledge graph–grounded dialog generation on OpenDialKG and KOMODIS datasets.

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Daeun Lee, Sejung Son, Hyolim Jeon, Seungbae Kim, Jinyoung Han

Multimodality

Challenge:	Aphasia is a language disorder caused by brain damage affecting speech functions . a detailed diagnosis of aphasia type is imperative for effective treatment . but, little attention has been paid to developing methods to detect different types of sphasis .
Approach:	They propose a multimodal graph neural network for aphasia type detection using co-speech gestures and corresponding speech and gesture patterns.
Outcome:	The proposed model outperforms existing methods in F1 and 84.2% of cases.

GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge? (2024.acl-long)

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Dayoon Ko, Jinyoung Kim, Hahyeon Choi, Gunhee Kim

Natural Language Interfaces

Challenge:	Existing knowledge-based datasets are outdated due to the rapid evolution of knowledge.
Approach:	They propose a retrieval-interactive language model framework that evaluates and reflects on its answers for further re-retrieval.
Outcome:	The proposed framework performs comparably to or surpasses continuously trained language models.

Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory (2024.findings-emnlp)

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Suyeon Lee, Sunghwan Kim, Minju Kim, Dongjin Kang, Dongil Yang, Harim Kim, Minseok Kang, Dayi Jung, Min Kim, Seungbeen Lee, Kyong-Mee Chung, Youngjae Yu, Dongha Lee, Jinyoung Yeo

Linguistics & Cognitive NLP

Challenge:	Existing models that use large language models are not available due to ethical concerns, and data privacy concerns are a concern.
Approach:	They propose a multi-turn dialogue dataset that emulates real-life counseling interactions using the goal-oriented approach of Cognitive Behavioral Therapy (CBT).
Outcome:	The proposed model outperforms other models in counseling skills, highlighting its effectiveness and potential as a counseling agent.

Chemical Language Understanding Benchmark (2023.acl-industry)

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Yunsoo Kim, Hyuk Ko, Jane Lee, Hyun Young Heo, Jinyoung Yang, Sungsoo Lee, Kyu-hwang Lee

Stop Playing the Guessing Game! Evaluating Conversational Recommender Systems via Target-free User Simulation (2025.findings-emnlp)

Challenge:	CLUB datasets are used to facilitate NLP research in the chemical industry.
Approach:	They introduce a benchmark dataset called CLUB to facilitate NLP research in the chemical industry.
Outcome:	The CLUB datasets are a new benchmark dataset for NLP in the chemical industry.

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SungHwan Kim, Kwangwook Seo, Tongyoung Kim, Jinyoung Yeo, Dongha Lee

Natural Language Interfaces

Challenge:	despite advances in CRSs, reliably assessing their ability to elicit preferences remains a challenge.
Approach:	They propose a user-CRS evaluation protocol with target-free user simulators . they show that current evaluation metrics emphasize single-turn recall of target items .
Outcome:	The proposed evaluation protocol is based on a simulation-based evaluation environment.

Detecting Bipolar Disorder from Misdiagnosed Major Depressive Disorder with Mood-Aware Multi-Task Learning (2024.naacl-long)

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Daeun Lee, Hyolim Jeon, Sejung Son, Chaewon Park, Ji hyun An, Seungbae Kim, Jinyoung Han

VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models (2024.acl-long)

Challenge:	Bipolar Disorder (BD) is a mental disorder characterized by intense mood swings, ranging from depression to manic states.
Approach:	They propose to use social media data to identify BD risk in individuals misdiagnosed as MDD by multi-task learning.
Outcome:	The proposed approach outperforms state-of-the-art baselines and can provide insights into the impact of BD mood on future risk.

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Seoyeon Kim, Kwangwook Seo, Hyungjoo Chae, Jinyoung Yeo, Dongha Lee

CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification (2023.eacl-demo)

Challenge:	Recent approaches in domain-specific named entity recognition (NER) have shown remarkable advances, but they still lack faithfulness, producing erroneous predictions.
Approach:	They propose a framework that revises errors from existing NER methods using knowledge to produce more faithful predictions.
Outcome:	The proposed framework can validate errors from existing models as a model-agnostic approach.

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Seungone Kim, Se June Joo, Yul Jang, Hyungjoo Chae, Jinyoung Yeo

Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents (2023.emnlp-main)

Challenge:	Chain-of-thought prompting generates an explanation before the final prediction, but its performance is affected by the factual accuracy of the explanation.
Approach:	They propose a toolkit for annotating the factual correctness of generated explanations and collecting revision data of wrong explanations.
Outcome:	The proposed toolkit is publicly available at https://github.com/SeungoneKim/CoTEVer.

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Hyungjoo Chae, Yongho Song, Kai Ong, Taeyoon Kwon, Minjin Kim, Youngjae Yu, Dongha Lee, Dongyeop Kang, Jinyoung Yeo

PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents (2025.findings-emnlp)

Challenge:	a human-like chatbot requires commonsense reasoning to comprehend and respond to information . however, identifying and aggregating key evidence within a single hop is a challenge . a knowledge distillation framework is proposed that leverages LLMs as unreliable teachers .
Approach:	They propose a framework that leverages large language models as unreliable teachers to facilitate multi-hop reasoning over a dialogue context.
Outcome:	The proposed framework leverages LLMs as unreliable teachers and selectively distills consistent and helpful rationales via alignment filters.

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Namyoung Kim, Kai Tzu-iunn Ong, Yeonjun Hwang, Minseok Kang, Iiseo Jihn, Gayoung Kim, Minju Kim, Jinyoung Yeo

CCQA: Generating Question from Solution Can Improve Inference-Time Reasoning in SLMs (2025.emnlp-main)

Challenge:	Existing strategies for proactive dialogue face limitations such as limited strategy coverage and preference bias in planning.
Approach:	They propose a synthetic strategy memory for proactive dialogue agents based on large language models . PRINCIPLES is derived through offline self-play simulations and serves as reusable knowledge that guides strategy planning during inference.
Outcome:	PRINCIPLES is a synthetic strategy memory for proactive dialogue agents.

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Jinyoung Kim, Ji Won Yoon

Reasoning

Challenge:	Recent advances in large language models (LLMs) have yielded remarkable performance across a wide range of tasks, including machine translation, code generation, sentiment analysis, and reasoning.
Approach:	They propose a new reasoning method that generates a question from each reasoning path and answer, evaluates each by its similarity to the original question, and selects the candidate solution with the highest similarity score as the final answer.
Outcome:	The proposed method outperforms existing state-of-the-art methods on mathematical and commonsense reasoning benchmarks and establishes a new practical baseline for efficient reasoning in SLMs.

Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy (2024.findings-acl)

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Jieyong Kim, Ryang Heo, Yongsik Seo, SeongKu Kang, Jinyoung Yeo, Dongha Lee

Can You Share Your Story? Modeling Clients’ Metacognition and Openness for LLM Therapist Evaluation (2025.findings-acl)

Challenge:	Recent studies have developed powerful generative methods for aspect sentiment quad prediction (ASQP) but they still suffer from imprecise predictions and limited interpretability due to data scarcity and inadequate modeling of the quadruplet composition process.
Approach:	They propose a self-consistent reasoning-based aspect sentiment quadruple prediction framework which generates reasonings and corresponding quadruples in sequence.
Outcome:	The proposed model significantly improves its ability to handle complex reasoning tasks and correctly predict quadruplets through consistency voting, resulting in enhanced interpretability and accuracy in aspect sentiment quadr uplp prediction.

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Minju Kim, Dongje Yoo, Yeonjun Hwang, Minseok Kang, Namyoung Kim, Minju Gwak, Beong-woo Kwak, Hyungjoo Chae, Harim Kim, Yunjoong Lee, Min Hee Kim, Dayi Jung, Kyong-Mee Chung, Jinyoung Yeo

Challenge:	Existing evaluation methods for psychological counseling rely on client simulators that clearly disclose internal states to the therapist, making it difficult to determine whether an LLM therapist can uncover unexpressed perspectives.
Approach:	They propose a new evaluation framework featuring a controllable and realistic client simulator which dynamically adapts itself based on the ongoing counseling session.
Outcome:	The proposed evaluation framework features a realistic and controllable client simulator which dynamically adapts itself based on the ongoing counseling session, offering a more realistic and challenging evaluation environment.

ToolHaystack: Stress-Testing Tool-Augmented Language Models in Realistic Long-Term Interactions (2025.findings-emnlp)

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Beong-woo Kwak, Minju Kim, Dongha Lim, Hyungjoo Chae, Dongjin Kang, Sunghwan Kim, Dongil Yang, Jinyoung Yeo

PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints (2026.findings-acl)

Challenge:	Existing evaluations assume tool use in short contexts, offering limited insight into model behavior during realistic long-term interactions.
Approach:	a benchmark is a tool to test long-term tool use in large language models . the tool includes multiple tasks execution contexts and realistic noise .
Outcome:	a new benchmark tests the tool use capabilities in long-term interactions.

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Minjun Park, Donghyun Kim, Hyeonjong Ju, Seungwon Lim, Dongwook Choi, Taeyoon Kwon, Minju Kim, Jinyoung Yeo

Towards Lifelong Dialogue Agents via Timeline-based Memory Management (2025.naacl-long)

Challenge:	Recent research explores multi-agent systems where agents collaborate toward shared goals to handle complex tasks.
Approach:	They propose a benchmark for systematic evaluation of multi-agent collaboration under privacy constraints.
Outcome:	The proposed benchmark shows that privacy constraints degrade collaboration performance and make outcomes depend more on the initiating agent than the partner.

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Kai Tzu-iunn Ong, Namyoung Kim, Minju Gwak, Hyungjoo Chae, Taeyoon Kwon, Yohan Jo, Seung-won Hwang, Dongha Lee, Jinyoung Yeo

Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models (2024.emnlp-main)

Challenge:	Existing studies focus on getting rid of outdated memories to improve retrieval quality, but we argue that such memories provide rich, important contextual cues for response generation (RG).
Approach:	They propose a framework for LLM-based lifelong dialogue agents that discards memory removal and manages large-scale memories by linking them based on their temporal and cause-effect relation.
Outcome:	The proposed framework augments RG with memory timelines based on evolution or causality of relevant past events.

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Hyungjoo Chae, Yeonghyeon Kim, Seungone Kim, Kai Ong, Beong-woo Kwak, Moohyeon Kim, Sunghwan Kim, Taeyoon Kwon, Jiwan Chung, Youngjae Yu, Jinyoung Yeo

Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics (2025.findings-naacl)

Challenge:	Prior work has used LLMs to generate programming language and applied external compilers for such tasks.
Approach:	They propose a framework that expresses task-level logic with pseudocode and tailors it to each instance and simulates execution of it.
Outcome:	The proposed framework outperforms baselines in diverse reasoning tasks.

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Seungbeen Lee, Seungwon Lim, Seungju Han, Giyeong Oh, Hyungjoo Chae, Jiwan Chung, Minju Kim, Beong-woo Kwak, Yeonsoo Lee, Dongha Lee, Jinyoung Yeo, Youngjae Yu

Challenge:	Recent advances in Large Language Models (LLMs) have led to their adaptation as conversational agents.
Approach:	They propose a new benchmark that uses 8K multi-choice questions to assess the personality of Large Language Models.
Outcome:	The proposed personality test outperforms existing personality tests for LLMs in reliability and validity.

Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization (2022.coling-1)

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Seungone Kim, Se June Joo, Hyungjoo Chae, Chaehyeong Kim, Seung-won Hwang, Jinyoung Yeo