Papers by Yohan Jo
Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information (2024.findings-naacl)
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| Challenge: | Prior studies have attempted to enhance faithfulness of abstractive summarization, yet hallucination remains a persistent challenge. |
| Approach: | They propose a decoding strategy that adjusts the generation probability of each token by comparing it with the token’s marginal probability within the domain of the source text. |
| Outcome: | The proposed method significantly improves faithfulness and source relevance on the XSUM dataset. |
KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy (2025.naacl-long)
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| Challenge: | Motivational Interviewing (MI) is gaining attention as a theoretical basis for mental health chatbots. |
| Approach: | They propose a framework that simulates MI sessions enriched with the expertise of professional therapists by using large language models to generate utterances through prompt engineering. |
| Outcome: | The proposed framework simulates MI sessions enriched with the expertise of professional therapists and employs large language models to generate utterances through prompt engineering. |
Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation (2020.lrec-1)
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| Challenge: | a corpus of 2016 debates and commentary contains 4,648 argumentative propositions annotated with fine-grained proposition types. |
| Approach: | They propose a machine learning-human workflow for annotating for four complex proposition types . they demonstrate with preliminary analysis of rhetorical strategies and structure in presidential debates . |
| Outcome: | The proposed method can be used by technical researchers seeking more nuanced representations of argument . it can also be used to analyze rhetorical strategies and structure in presidential debates . |
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction (2025.acl-long)
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| Challenge: | Multiple-choice questions (MCQs) are critical for identifying misconceptions and gaps in knowledge and accurately assessing students' understanding. |
| Approach: | They propose to train a model to generate distractors that are more likely to be selected by students by a pairwise ranker and a distractor generator via Direct Preference Optimization. |
| Outcome: | The proposed model outperforms baseline models and performs comparable to humans in various metrics including pairwise rank accuracy and distractor plausibility. |
Quantifying Data Contamination in Psychometric Evaluations of LLMs (2026.findings-eacl)
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| Challenge: | Existing studies have raised concerns about data contamination from psychometric inventories . however, there is no systematic attempt to quantify the extent of data contamination . |
| Approach: | They propose a framework to measure data contamination in psychometric evaluations of Large Language Models by item memorization, evaluation memorisation and target score matching. |
| Outcome: | The proposed framework evaluates item memorization, evaluation memorisation, and target score matching in 21 models from major families and four widely used psychometric inventories. |
KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models (2023.findings-emnlp)
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| Challenge: | Using large language models for complex reasoning tasks on knowledge graphs remains unexplored. |
| Approach: | They propose a multi-purpose framework leveraging large language models for complex reasoning tasks on knowledge graphs. |
| Outcome: | The proposed framework outperforms fully-supervised models in KG-based fact verification and KGQA benchmarks. |
Knowledge Tracing in Programming Education Integrating Students’ Questions (2025.acl-long)
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| Challenge: | Existing knowledge tracing models that ignore student questions are suboptimal for programming education because of the complexity of coding tasks and the diverse methods students use to solve problems. |
| Approach: | They propose a model that leverages students' questions and automatically extracted skill information to enhance the accuracy of predicting students' performance on subsequent problems. |
| Outcome: | The proposed model improves student performance across Python programming courses of differing difficulty levels. |
Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users (2023.findings-emnlp)
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Yohan Jo, Xinyan Zhao, Arijit Biswas, Nikoletta Basiou, Vincent Auvray, Nikolaos Malandrakis, Angeliki Metallinou, Alexandros Potamianos
| Challenge: | a dataset of task-oriented dialogues assume conversations between the agent and one user at a time . but multi-user task-orientated dialogues are richer, containing deliberation and deliberations . a novel task is proposed to rewrite a task-focused query that retains only task-relevant information . |
| Approach: | They propose to rewrite a task-oriented chat between two users as a concise task-orientated query that retains only task-relevant information and is directly consumable by the dialogue system. |
| Outcome: | The proposed method surpasses existing models on multi-user dialogues and generalizes to unseen domains. |
Knowledge-Enhanced Evidence Retrieval for Counterargument Generation (2021.findings-emnlp)
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| Challenge: | Existing models lack the reasoning abilities needed to find complex counterevidence. |
| Approach: | They propose a natural language inference model that finds counterevidence from diverse sources on the Web. |
| Outcome: | The proposed model outperforms baseline models for NLI tasks and finds complex counterevidence better. |
Don’t Adapt Small Language Models for Tools; Adapt Tool Schemas to the Models (2026.acl-long)
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| Challenge: | Small language models struggle with tool-use tasks, particularly in selecting appropriate tools and identifying correct parameters. |
| Approach: | They propose a training-free method that leverages peakedness to align schemas with pretraining knowledge to rename tool components. |
| Outcome: | Experiments on MetaTool and RoTBench show that PA-Tool significantly improves tool-use accuracy without retraining. |
Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes (2024.findings-acl)
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Sunjun Kweon, Junu Kim, Jiyoun Kim, Sujeong Im, Eunbyeol Cho, Seongsu Bae, Jungwoo Oh, Gyubok Lee, Jong Hak Moon, Seng Chan You, Seungjin Baek, Chang Hoon Han, Yoon Bin Jung, Yohan Jo, Edward Choi
| Challenge: | Clinical notes are an extensive repository of information specific to individual patients. |
| Approach: | They create synthetic large-scale clinical notes using publicly available case reports extracted from biomedical literature and train a clinical large language model, Asclepius. |
| Outcome: | The proposed model outperforms several other models and is supported by detailed evaluations conducted by GPT-4 and medical professionals. |
SpeakerSleuth: Can Large Audio-Language Models Judge Speaker Consistency across Multi-turn Dialogues? (2026.acl-long)
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| Challenge: | Large Audio-Language Models (LALMs) are a popular approach for evaluating speech quality, yet their ability to assess speaker consistency across multi-turn dialogues remains unexplored. |
| Approach: | They construct 1,818 human-verified evaluation instances across four datasets spanning synthetic and real speech, with controlled acoustic difficulty. |
| Outcome: | The proposed model performs better in comparing and ranking acoustic variants, demonstrating inherent acustic discrimination capabilities. |
Attentive Interaction Model: Modeling Changes in View in Argumentation (N18-1)
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| Challenge: | Prior work on argumentation in the NLP community has focused mainly on the first goal and has missed more nuanced and complex details of viewpoints. |
| Approach: | They propose a neural architecture that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument to predict if the argument succeeded in altering the OH' s view. |
| Outcome: | The proposed model outperforms several baselines on discussions on the Change My View forum on Reddit. |
Detecting Attackable Sentences in Arguments (2020.emnlp-main)
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| Challenge: | Prior work in NLP studies focus on argument quality and making counterarguments toward the main claim, without investigating what parts of an argument are attackable for successful persuasion. |
| Approach: | They propose to use machine learning to find attackable sentences in online arguments by analyzing driving reasons for attacks and identifying relevant characteristics of sentences. |
| Outcome: | The proposed model can detect attackable sentences significantly better than baselines and comparably well to laypeople. |
Extracting Implicitly Asserted Propositions in Argumentation (2020.emnlp-main)
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| Challenge: | Argumentation is a rhetorical device that asserts propositions implicitly, but few studies have examined the issue. |
| Approach: | They propose a computational method for extracting propositions that are implicitly asserted in questions, reported speech, and imperatives in argumentation. |
| Outcome: | The proposed models are based on a corpus of 2016 debates and online commentary. |
FactKG: Fact Verification via Reasoning on Knowledge Graphs (2023.acl-long)
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| Challenge: | knowledge graphs (KGs) have not been fully utilized as a knowledge source for fact verification. |
| Approach: | They propose a dataset to enable the community to better use knowledge graphs . they propose 108k natural language claims with five types of reasoning . |
| Outcome: | The proposed dataset consists of 108k natural language claims with five types of reasoning . authors believe the proposed method can advance reliability and practicality . |
Value Portrait: Assessing Language Models’ Values through Psychometrically and Ecologically Valid Items (2025.acl-long)
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| Challenge: | Existing benchmarks rely on human annotations that are vulnerable to value-related biases. |
| Approach: | They propose a value portrait benchmark that uses items that capture real-life user-LLM interactions and a rated item based on its similarity to their own thoughts to determine reliability. |
| Outcome: | The proposed framework improves the relevance of assessment results to real-world LLM usage by allowing human subjects to rate items with similarity to their own thoughts and derived correlations between these ratings and the subjects’ actual value scores. |
Argument Mining for Review Helpfulness Prediction (2022.emnlp-main)
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| Challenge: | Argumentational features have been shown to be promising indicators of product review helpfulness, but their utility has been limited due to the lack of resources and large-scale experiments investigating their utility. |
| Approach: | They present an argumentational argumentation model that annotates 878 Amazon reviews on headphones and uses it to evaluate argument quality. |
| Outcome: | The proposed model improves the state-of-the-art model under text-only and text-and-image settings. |
Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes (2021.tacl-1)
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| Challenge: | Recent studies have focused on training complex neural networks on labeled data. |
| Approach: | They propose to use logical mechanisms to classify argumentative relations without training on labeled data. |
| Outcome: | The proposed method classifies argumentative relations without training on labeled data significantly better than unsupervised baselines. |
ThinkBrake: Efficient Reasoning via Log-Probability Margin Guided Decoding (2026.findings-acl)
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| Challenge: | Recent advances in Large Reasoning Models (LRMs) have demonstrated remarkable capabilities across various tasks. |
| Approach: | They propose a system that stops reasoning when the margin between continuation token and lt;/think gt; narrows. |
| Outcome: | The proposed model reduces thinking token usage by 30% and improves accuracy by 8% while reducing thinking tokens by 72%. |
Status Biases in Deliberation Online: Evidence from a Randomized Experiment on ChangeMyView (2022.findings-emnlp)
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| Challenge: | Status is widely used to incentivize user engagement, but visible status indicators could inadvertently bias online deliberation to favor high-status users. |
| Approach: | They propose to quantify status biases in online deliberation using a ChangeMyView platform and to test whether status visibility can inadvertently bias it to favor high-status users. |
| Outcome: | The proposed method increases the persuasion rate of moderate-status users by 84% and lowers the per-su-sion rate for high-statuse users by 41% relative to the control group. |
Dialogue Systems for Emotional Support via Value Reinforcement (2025.acl-long)
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| Challenge: | Emotional support dialogue systems aim to reduce help-seekers’ distress and help them overcome challenges. |
| Approach: | They propose a value-driven method for training emotional support dialogue systems designed to reinforce positive values in seekers by leveraging online support conversations from Reddit. |
| Outcome: | The proposed model outperforms baseline models across support skills, seekers’ emotional intensity, and value reinforcement. |
PVP: An Image Dataset for Personalized Visual Persuasion with Persuasion Strategies, Viewer Characteristics, and Persuasiveness Ratings (2025.acl-long)
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| Challenge: | Visual persuasion uses visual elements to influence cognition and behaviors . lack of comprehensive data sets connect persuasiveness of images with personal information . |
| Approach: | They propose to use a dataset to connect persuasiveness with personal information . they find psychological characteristics enhance the generation and evaluation of persuasive images . |
| Outcome: | The proposed dataset provides persuasiveness scores of images evaluated by human annotators along with demographic and psychological characteristics. |
Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly used in decision-support applications that aim to influence human behavior or beliefs, such as health coaching, tutoring, and targeted marketing. |
| Approach: | They propose a context-aware user profiling framework with two trainable components that generate optimal queries to retrieve persuasion-relevant records from a user’s history and a profiler that summarizes these records into a model. |
| Outcome: | The proposed framework raises F1 from 33% to 47% on Llama-3.3-70B-Instruct. |
Stress-Testing Emotional Support Models: Moving from Homogeneous to Diverse Help Seekers (2026.findings-acl)
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| Challenge: | Existing simulators fail to capture behavioral diversity of real-world seekers . lack of reliable automated evaluation frameworks hinders field's establishment . |
| Approach: | They propose a controllable seeker simulator driven by nine psychological and linguistic features that underpin seeker behavior. |
| Outcome: | The proposed model achieves superior profile adherence and behavioral diversity compared to existing approaches. |
From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models (2023.emnlp-main)
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| Challenge: | Existing large-scale surveys soliciting opinions on issues can be costly and laborious. |
| Approach: | They propose to use value-injected large language models to inject a target value distribution into large language model (LLM) and have them predict opinions and behaviors of people with similar values. |
| Outcome: | The proposed method significantly outperforms baseline methods on four tasks and the results suggest opinions and behaviors can be better predicted using value-injected LLMs. |
The Open Argument Mining Framework (2025.acl-demo)
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Debela Gemechu, Ramon Ruiz-Dolz, Kamila Górska, Somaye Moslemnejad, Eimear Maguire, Dimitra Zografistou, Yohan Jo, John Lawrence, Chris Reed
| Challenge: | Argument Mining (AM) has been a key area of research for many years, but it is still a challenging field. |
| Approach: | the oAMF provides an open-source, modular platform that unifies diverse AM methods. |
| Outcome: | the oAMF is an open-source, modular, and scalable platform that unifies diverse AM methods. |
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. |
Open-WikiTable : Dataset for Open Domain Question Answering with Complex Reasoning over Table (2023.findings-acl)
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| Challenge: | Open-WikiTable is the first open domain question answering dataset that requires complex reasoning over tables. |
| Approach: | They propose to use open-domain question answering over tables to extract questions from tables. |
| Outcome: | The dataset is publicly available. it is built upon WikiSQL and WikiTableQuestions. |
Ask Optimal Questions: Aligning Large Language Models with Retriever’s Preference in Conversation (2025.findings-naacl)
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| Challenge: | Existing methods to perform conversational search are sub-optimal due to the limited ability to incorporate signals from the retrieval results. |
| Approach: | They propose to optimize a language model for reformulating search queries in line with retrievers’ preferences by combining a large-scale dataset with Retrievers’ Feedback. |
| Outcome: | The proposed framework outperforms existing methods on two benchmarks and surpasses the state-of-the-art methods. |
Improving Dialogue State Tracking through Combinatorial Search for In-Context Examples (2025.acl-long)
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| Challenge: | Existing methods for training dialogue state tracking data are suboptimal . existing methods rely on suboptimized data, resulting in poor performance . |
| Approach: | They propose a method that scores effective in-context examples based on their combinatorial impact on DST performance. |
| Outcome: | The proposed method achieves a 20% gain in data efficiency and generalizing well to the SGD dataset. |
Model-based Preference Optimization in Abstractive Summarization without Human Feedback (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) can generate fluent summaries but often introduce inaccuracies by hallucinating content not found in the source document. |
| Approach: | They propose a method to fine-tune Large Language Models for improved summarization abilities without any human feedback. |
| Outcome: | The proposed method significantly improves the quality of generated summaries without any human feedback. |
Context-Robust Knowledge Editing for Language Models (2025.findings-acl)
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| Challenge: | Existing knowledge editing methods assess success by considering only edited knowledge without preceding contexts. |
| Approach: | They propose a method to strengthen context robustness by minimizing context-sensitive variance in hidden states of the model. |
| Outcome: | The proposed method improves the success rate in situations where a preceding context is present and preserves the overall capabilities of the model. |