Papers by Jungseul Ok
Revisiting Early Detection of Sexual Predators via Turn-level Optimization (2025.naacl-long)
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| Challenge: | Existing methods to detect online grooming rely on chat-level risk labels and fail to identify optimal intervention points. |
| Approach: | They propose a speed control reinforcement learning strategy based on luring communication theory to capture the predator’s turn-level entrapment and a new reward function that balances the trade-off between speed and accuracy based upon the LCT. |
| Outcome: | The proposed method preempts online grooming while identifying optimal early intervention points. |
Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients (2025.acl-long)
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| Challenge: | Existing methods for federated fine-tuning for Large Language Models suffer from performance degradation at low ranks in heterogeneous data settings. |
| Approach: | They propose a low-rank adaptive model with Alternating freeze and Adaptive rank selection which reduces the number of uploaded parameters by 99.8% . |
| Outcome: | The proposed low-rank Adaptation maintains robustness even under extreme heterogeneity and low rank conditions while preserving communication efficiency. |
Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models (2025.acl-long)
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| Challenge: | Existing methods for multi-step reasoning suffer from inefficiency and redundancy . existing methods neglect the diversity of task difficulties leading to extensive searches even for easy tasks . |
| Approach: | They propose a method that explores reasoning paths with a gating mechanism that decides whether to conduct a tree search based on the confidence level of answers from a previous simple reasoning method. |
| Outcome: | The proposed method significantly improves accuracy by 4.3% on average while requiring only 31% of computational costs. |
PaT: Planning-after-Trial for Efficient Test-Time Code Generation (2026.acl-long)
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| Challenge: | Existing methods for scaling test-time computation are rigid and inefficient . a heterogeneous configuration achieves performance comparable to a large homogeneously model . |
| Approach: | They propose an adaptive planning policy that invokes a planner only upon verification failure. |
| Outcome: | The proposed model achieves comparable performance to a large homogeneous model while reducing inference cost by approximately 69% across multiple benchmarks and model families. |
Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search (2026.acl-long)
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| Challenge: | Controllable summarization is a form of outputs that tailors summaries to user-specified attributes. |
| Approach: | They propose an adaptive planning framework that reframes the task as planning the order of sequential attribute control with a customized Monte Carlo Tree Search. |
| Outcome: | The proposed framework surpasses LLM-based self-planning models and fine-tuned baselines in multi-attribute controllable summarization. |
Efficient Latent Semantic Clustering for Scaling Test-Time Computation of LLMs (2025.findings-emnlp)
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| Challenge: | Existing methods for scaling test-time computation rely on external models that introduce substantial computational overhead and fail to capture context-aware semantics. |
| Approach: | They propose a method that leverages the generator LLM’s internal hidden states for clustering, eliminating the need for external models. |
| Outcome: | The proposed method improves the computational efficiency of test-time scaling while maintaining or exceeding the performance of existing methods. |
Self-Training Large Language Models with Confident Reasoning (2025.findings-emnlp)
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| Challenge: | Large language models generate reasoning paths before final answers, but learning such a path requires costly human supervision. |
| Approach: | They propose a method that fine-tunes LLMs to prefer reasoning paths with high confidence . they propose 'cORE-PO' that fine tunes Lms to choose high-quality reasoning paths . |
| Outcome: | The proposed method improves the accuracy of outputs on four in-distribution and two out-of-difference benchmarks. |
DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition (2025.naacl-long)
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| Challenge: | Existing studies have focused on data augmentation and feature extraction methods to improve dysarthric speech recognition. |
| Approach: | They propose a Dynamic Phoneme-level Contrastive Learning method which decomposes the speech utterance into phoneme segments for phoneme- level contrastive learning. |
| Outcome: | The proposed method outperforms baseline models and achieves an average 22.10% reduction in word error rate (WER) across the overall dysarthria group. |
Multi-Dimensional Optimization for Text Summarization via Reinforcement Learning (2024.acl-long)
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| Challenge: | Existing summarization methods target a specific dimension, resulting in poor quality summaries. |
| Approach: | They propose multi-objective reinforcement learning tailored to generate balanced summaries across all dimensions. |
| Outcome: | The proposed model achieves significant performance gains compared to baseline models on representative summarization datasets on four dimensions. |
Bridging the Gap between Expert and Language Models: Concept-guided Chess Commentary Generation and Evaluation (2025.naacl-long)
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| Challenge: | Experimental results show that expert models generate accurate, informative and fluent commentary, but are prone to hallucinations due to their limited decision-making capabilities. |
| Approach: | They propose a concept-guided chess commentary generation and a GPT-based Chess Commentary Evaluation to bridge this gap between expert models and large language models. |
| Outcome: | The proposed model is accurate, informative, and fluent. |
Retrieval-Augmented Generation with Estimation of Source Reliability (2025.emnlp-main)
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| Challenge: | Retrieval-Augmented Generation (RAG) is an effective approach to enhance the factual accuracy of large language models (LLMs). |
| Approach: | They propose a multi-source RAG framework that estimates the reliability of sources and prioritizes highly reliable and relevant documents. |
| Outcome: | The proposed framework outperforms baselines in scenarios with heterogeneous source reliability while scaling efficiently as the number of sources increases. |
MiLQ: Benchmarking IR Models for Bilingual Web Search with Mixed Language Queries (2025.emnlp-main)
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| Challenge: | Information Retrieval (IR) research on mixed-language queries remains sparse and outdated. |
| Approach: | They propose a test set for mixed-language queries that is realistic and preferred by bilingual speakers. |
| Outcome: | The proposed benchmarks show that multilingual IR models perform moderately on MiLQ and inconsistently across native, English, and mixed-language queries. |
Exploring Iterative Controllable Summarization with Large Language Models (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) excel at abstractive summarization tasks, but their ability to precisely control summary attributes remains underexplored. |
| Approach: | They propose a guide-to-explain framework for controllable summarization that enables the model to identify misaligned attributes in the initial draft and guides it to self-explan errors in the previous output. |
| Outcome: | The proposed framework generates well-adjusted summaries that satisfy the desired attributes with robust effectiveness while requiring surprisingly fewer iterations than other iterative approaches. |
Comparison-based Active Preference Learning for Multi-dimensional Personalization (2025.acl-long)
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| Challenge: | Large language models have shown remarkable success, but aligning them with human preferences remains a core challenge. |
| Approach: | They propose to capture implicit user preferences from comparative feedback to improve model performance. |
| Outcome: | The proposed framework is able to capture implicit user preferences from comparative feedback. |
DeRAGEC: Denoising Named Entity Candidates with Synthetic Rationale for ASR Error Correction (2025.findings-acl)
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| Challenge: | Recent studies have demonstrated that postprocessing speech recognition transcriptions with large language models can significantly enhance the accuracy of Automatic Speech Recognition (ASR). |
| Approach: | They propose a method to improve Named Entity (NE) correction in Automatic Speech Recognition systems by leveraging phonetic similarity and augmented definitions. |
| Outcome: | The proposed method outperforms baseline methods on common voice and STOP datasets and achieves a 28% reduction in WER and NE hit ratio. |
CoPL: Collaborative Preference Learning for Personalizing LLMs (2025.emnlp-main)
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| Challenge: | Existing methods for personalizing large language models struggle with flexibility and generalization. |
| Approach: | They propose a graph-based collaborative filtering framework that models user-response relationships to enhance preference estimation in sparse annotation settings. |
| Outcome: | The proposed framework outperforms existing reward models in TL;DR, UltraFeedback-P, and PersonalLLM datasets. |