Papers by Jinyoung Yeo
Commonsense-augmented Memory Construction and Management in Long-term Conversations via Context-aware Persona Refinement (2024.eacl-short)
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| 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. |
BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets (2022.emnlp-main)
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| 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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| 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
| 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
| 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. |
Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset (2024.findings-acl)
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Minjin Kim, Minju Kim, Hana Kim, Beong-woo Kwak, SeongKu Kang, Youngjae Yu, Jinyoung Yeo, Dongha Lee
| 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. |
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning (2022.naacl-main)
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| 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. |
Soft Representation Learning for Sparse Transfer (P19-1)
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| Challenge: | Using adversarial training, we can “soft-code” shared and private spaces to avoid sparse sharing. |
| Approach: | They propose to use adversarial training to “soft-code” shared and private spaces to avoid the shared space gets too sparse. |
| Outcome: | The proposed architecture avoids sparse sharing of shared and private spaces, and also deals with low-quality input. |
Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering (2024.emnlp-main)
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| Challenge: | Existing methods for enhancing QA performance of Large Language Models (LLMs) have limitations, including duplicated entities or relations, reduced evidence density, and failure to highlight crucial evidence. |
| Approach: | They propose an Evidence-focused Fact Summarization framework for enhanced QA with knowledge-augmented Large Language Models (LLMs) that incorporates external knowledge into LLMs to improve QA performance. |
| Outcome: | The proposed framework improves LLM’s zero-shot QA performance especially when noisy facts are retrieved. |
Visual Choice of Plausible Alternatives: An Evaluation of Image-based Commonsense Causal Reasoning (L18-1)
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Jinyoung Yeo, Gyeongbok Lee, Gengyu Wang, Seungtaek Choi, Hyunsouk Cho, Reinald Kim Amplayo, Seung-won Hwang
| Challenge: | Existing methods for evaluating plausibility of events are focused on measuring causal dependency between events or actions. |
| Approach: | They propose a task to identify the more plausible alternative with their commonsense causal context. |
| Outcome: | The proposed task is based on a visual COPA dataset with 380 questions and over 1K images with various topics. |
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
| 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. |
Less is More: Attention Supervision with Counterfactuals for Text Classification (2020.emnlp-main)
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| Challenge: | Specifically, we explore the advantage of counterfactual reasoning, over associative reasoning . Adding human supervision to attention has been shown to improve model predictions and explanations . |
| Approach: | They propose to use machine-augmented human attention supervision to enhance model quality. |
| Outcome: | The proposed method is more effective than existing methods requiring higher annotation cost . the proposed method can be trained to generate similar attention to human supervision . |
Stop Playing the Guessing Game! Evaluating Conversational Recommender Systems via Target-free User Simulation (2025.findings-emnlp)
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| 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. |
Unveiling Implicit Table Knowledge with Question-Then-Pinpoint Reasoner for Insightful Table Summarization (2024.findings-emnlp)
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| Challenge: | a novel table reasoning framework is needed to uncover the hidden knowledge hidden within the explicit table cells. |
| Approach: | They propose a table reasoning framework Question-then-pinpoint that can self-question table knowledge and answer it faithfully. |
| Outcome: | The proposed framework can self-question and answer the knowledge by pinpointing evidence on the table. |
Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code (2024.emnlp-main)
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Hyungjoo Chae, Taeyoon Kwon, Seungjun Moon, Yongho Song, Dongjin Kang, Kai Ong, Beong-woo Kwak, Seonghyeon Bae, Seung-won Hwang, Jinyoung Yeo
| Challenge: | Large language models (LLMs) have made great progress in code generation, however, they still produce errors. |
| Approach: | They propose a RL environment that provides feedback on code editing by analyzing the performance of the revised code in unit tests. |
| Outcome: | The proposed model outperforms baselines in enhancing open-source code LLMs’ code editing, making them comparable with closed-source LLM. |
VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models (2024.acl-long)
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| 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. |
CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification (2023.eacl-demo)
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| 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. |
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents (2023.emnlp-main)
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Hyungjoo Chae, Yongho Song, Kai Ong, Taeyoon Kwon, Minjin Kim, Youngjae Yu, Dongha Lee, Dongyeop Kang, Jinyoung Yeo
| 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. |
RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization (2024.naacl-demo)
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| Challenge: | Abstractive summarization has emerged as a critical tool in the era of information overload. |
| Approach: | They propose an unsupervised summarization framework that utilizes relation triples as the basic unit for summarizing. |
| Outcome: | The proposed framework visualizes salience levels for sentences, relation triples, and phrases. |
Evidentiality-aware Retrieval for Overcoming Abstractiveness in Open-Domain Question Answering (2024.findings-eacl)
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| Challenge: | Existing approaches to ODQA use a simple yet effective retriever-reader framework, but this approach is not always effective in abstractive tasks. |
| Approach: | They propose a method that leverages synthetic distractor samples to learn to discriminate evidence passages from distractors. |
| Outcome: | The proposed method is validated on multiple abstractive open-domain question answering tasks. |
PRINCIPLES: Synthetic Strategy Memory for Proactive Dialogue Agents (2025.findings-emnlp)
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Namyoung Kim, Kai Tzu-iunn Ong, Yeonjun Hwang, Minseok Kang, Iiseo Jihn, Gayoung Kim, Minju Kim, Jinyoung Yeo
| 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. |
Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy (2024.findings-acl)
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| 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. |
Can You Share Your Story? Modeling Clients’ Metacognition and Openness for LLM Therapist Evaluation (2025.findings-acl)
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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. |
Learning with Limited Data for Multilingual Reading Comprehension (D19-1)
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| Challenge: | Existing approaches to support question answering in a new language with limited training resources introduce noises to the training data due to translation or generation errors. |
| Approach: | They propose a weakly-supervised framework that quantifies noises from automatically generated labels to deemphasize or fix noisy data in training. |
| Outcome: | The proposed framework can deemphasize or fix noisy data in training on low-resource languages with varying similarity to English. |
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
| 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. |
PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints (2026.findings-acl)
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Minjun Park, Donghyun Kim, Hyeonjong Ju, Seungwon Lim, Dongwook Choi, Taeyoon Kwon, Minju Kim, Jinyoung Yeo
| 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. |
Towards Lifelong Dialogue Agents via Timeline-based Memory Management (2025.naacl-long)
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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
| 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. |
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models (2024.emnlp-main)
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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
| 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. |
Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics (2025.findings-naacl)
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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. |
Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths (2026.findings-acl)
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| Challenge: | Generative retrieval directly decodes a document identifier, making it impossible to provide explanations for its retrieval decision. |
| Approach: | They propose a hierarchical category path-Enhanced Generative Retrieval that generates category paths step-by-step and decodes docid. |
| Outcome: | The proposed method provides explanations for retrieval decision by generating hierarchical category paths step-by-step and decoding docid. |
Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization (2022.coling-1)
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| Challenge: | Existing frameworks that use commonsense as supervision only use input knowledge, but it generates more informative and consistent summaries. |
| Approach: | They propose to leverage the unique characteristics of dialogues sharing commonsense knowledge to solve the difficulties in summarizing them. |
| Outcome: | The proposed framework generates more informative and consistent summaries with injected commonsense knowledge than existing methods. |
Rethinking Reward Model Evaluation Through the Lens of Reward Overoptimization (2025.acl-long)
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| Challenge: | Existing benchmarks for reward models show a weak correlation with performance of optimized policies . existing benchmarks do not accurately assess the true capabilities of reward models . |
| Approach: | They explore how reward overoptimization captures how well a reward model aligns with human preferences and the dynamics of the learning signal it provides to the policy. |
| Outcome: | The proposed benchmarks show that reward overoptimization is a weak factor . the high correlation with degree of overoptimalization leads to lower correlation with downstream performance . |