Papers by Yohan Lee
KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy (2025.naacl-long)
Copied to clipboard
| 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. |
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction (2025.acl-long)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
More Insightful Feedback for Tutoring: Enhancing Generation Mechanisms and Automatic Evaluation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Incorrect student answers can be valuable learning opportunities provided that the student understands where they went wrong and why. |
| Approach: | They propose to use a KL regularization term to achieve more targeted input representations and a preference optimization step to encourage student answer-adaptive feedback generation. |
| Outcome: | The proposed model outperforms existing models in 3.3 METEOR points. |
Finding Diamonds in Conversation Haystacks: A Benchmark for Conversational Data Retrieval (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Our work identifies unique challenges in conversational data retrieval . large language model-based systems operate through open-ended interactions without predefined specifications. |
| Approach: | They propose a benchmark to evaluate systems that retrieve conversation data for product insights. |
| Outcome: | The benchmark provides a reliable standard for measuring conversational data retrieval performance. |
SentiCSE: A Sentiment-aware Contrastive Sentence Embedding Framework with Sentiment-guided Textual Similarity (2024.lrec-main)
Copied to clipboard
| Challenge: | Sentiment-aware pre-trained language models (PLMs) demonstrate impressive results in downstream sentiment analysis tasks, but they neglect to evaluate the quality of constructed sentiment representations. |
| Approach: | They propose a new metric for evaluating the quality of sentiment representations that is based on the degree of equivalence in sentiment polarity between two sentences. |
| Outcome: | The proposed framework outperforms the existing sentiment-aware models in sentiment analysis tasks. |
FEAT: A Preference Feedback Dataset through a Cost-Effective Auto-Generation and Labeling Framework for English AI Tutoring (2025.acl-short)
Copied to clipboard
| Challenge: | Existing algorithms for teacher feedback generation are time-consuming and costly to generate manually. |
| Approach: | They propose a framework for generating teacher feedback using LLMs and humans . they construct three datasets that are time-consuming and costly to generate manually . results show that incorporating a small portion of DM leads to superior performance . |
| Outcome: | The proposed framework performs better on three datasets compared to human-generated feedback and LLM-generated datasets. |
Don’t Adapt Small Language Models for Tools; Adapt Tool Schemas to the Models (2026.acl-long)
Copied to clipboard
| 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. |
Improving End-to-End Task-Oriented Dialog System with A Simple Auxiliary Task (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Using large pre-trained language models for end-to-end TOD modeling has made significant progress on benchmarks . a paradigm of leveraging large pretrained models has shown promising results . |
| Approach: | They combine paradigm of leveraging large pre-trained language models with multi-task learning framework . their model achieves new state-of-the-art results with combined scores of 108.3 and 107.5 . |
| Outcome: | The proposed model achieves state-of-the-art results on multiWOZ 2.0 and MultiWOZ 2.1 . it also improves generalization capability through domain adaptation experiments in the few-shot setting. |
Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes (2024.findings-acl)
Copied to clipboard
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)
Copied to clipboard
| 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. |
Value Portrait: Assessing Language Models’ Values through Psychometrically and Ecologically Valid Items (2025.acl-long)
Copied to clipboard
| 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. |
ThinkBrake: Efficient Reasoning via Log-Probability Margin Guided Decoding (2026.findings-acl)
Copied to clipboard
| 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%. |
AMACE: Automatic Multi-Agent Chart Evolution for Iteratively Tailored Chart Generation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have shown that chart generation requires manual input of code and intent . despite the benefits of large language models, chart generation still requires user input of many parameters . |
| Approach: | They propose a loop-based framework for automatically evolving charts in a multi-agent environment using large language models. |
| Outcome: | The proposed framework improves performance by 29.97% compared to first generation while reducing generation time by 86.9% compared with manual prompt-based methods. |
Dialogue Systems for Emotional Support via Value Reinforcement (2025.acl-long)
Copied to clipboard
| 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. |
What Really Matters in Many-Shot Attacks? An Empirical Study of Long-Context Vulnerabilities in LLMs (2025.acl-long)
Copied to clipboard
| Challenge: | Recent advances in context length extension have improved LLMs' performance and safety, but they present critical safety challenges. |
| Approach: | They investigate long-context vulnerabilities in Large Language Models (LLMs) using many-shot jailbreaking to exploit context length extension. |
| Outcome: | The proposed attacks do not require carefully crafted harmful content. |
CTRL: Control-Based Time Series Forecasting with LLM-Guided Residual Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing time series forecasting approaches reduce them to numerical predictors that bypass their strengths or allow direct forecast generation that destabilizes predictions in non-stationary settings. |
| Approach: | They propose a framework that decouples semantic reasoning from quantitative prediction. |
| Outcome: | The proposed framework decouples semantic reasoning from quantitative prediction. |
PVP: An Image Dataset for Personalized Visual Persuasion with Persuasion Strategies, Viewer Characteristics, and Persuasiveness Ratings (2025.acl-long)
Copied to clipboard
| 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. |
HEISIR: Hierarchical Expansion of Inverted Semantic Indexing for Training-free Retrieval of Conversational Data using LLMs (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for retrieving dialogue data often face challenges in capturing semantic intent or require extensive labeling and fine-tuning. |
| Approach: | They propose a framework that enhances semantic understanding in conversational data retrieval by optimizing data ingestion. |
| Outcome: | The proposed framework outperforms fine-tuned models in retrieval and intent analysis. |
Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models place excessive reasoning burden on response generation, causing performance degradation . we present a new approach that shifts complex reasoning processes from inference to memory construction . |
| Approach: | a new approach shifts complex reasoning processes from inference to memory construction . PREMem extracts fine-grained memory fragments and establishes explicit relationships . small models achieve comparable results even with limited token budgets . |
| Outcome: | The proposed approach reduces the computational burden of response generation by capturing evolution patterns in memory items. |
Towards Lifelong Dialogue Agents via Timeline-based Memory Management (2025.naacl-long)
Copied to clipboard
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. |