3 citations · 6 across the 26 of their papers we have counts for
31 papers
ThinkFuse: Trajectory-Aware Test-Time Fusion for Small Reasoning Models
Myunghoon Kang, Jungseob Lee, Jaehyung Seo +1
Small reasoning models (SRMs) have shown strong performance on complex reasoning tasks by generating extended chain-of-thought trajectories, but they often fail to recover once the…
Predicting and Repairing Merge Collapse in Large Language Models
Jungseob Lee, Seungyoon Lee, Sugyeong Eo +3
Large language models fine-tuned from a shared base can be merged by averaging their task vectors, but some merges collapse far below the base model, and common merge operators giv…
Distilling Directional Verification
Jungseob Lee, Sugyeong Eo, Seongtae Hong +4
Knowledge distillation aims to transfer the factual knowledge of large language models to smaller models for efficient deployment. Yet a teacher may recall a relation in one direct…
PILAR: A Page-Grounded Unified Evidence Representation via an Entity-Linked Assertion Graph for Open-Domain QA Agents over Multimodal Document Corpora
Joongmin Shin, Gyuho Shim, Jung-hun Lee +1
Open-domain question answering (ODQA) over multimodal document corpora requires linking evidence scattered across text, tables, and figures. Existing systems often store these sour…
Vision Is Not Overhead: One-Pass Block Drafting for Lossless Speculative Decoding in Vision-Language Models
Jungseob Lee, Seongtae Hong, Dongyub Jude Lee +4
Speculative decoding accelerates generation without changing its output, but on vision-language models (VLMs) a self-reinforcing cycle holds it back. Because an autoregressive draf…
The Hallucination Signal Is a Mean Shift: Why Simple Probes Suffice
Jungseob Lee, Jaehyung Seo, Heuiseok Lim
Hidden-state probes effectively detect LLM hallucinations, but the geometry of the signal remains poorly characterized, driving increasingly complex probe architectures. Across thr…
CultureConverse: A Multilingual Multi-turn Simulation Harness for Culturally Grounded Assistance in East and Southeast Asia
Bryan Chen Zhengyu Tan, Weihua Zheng, Thong T. Doan +30
Current cultural evaluations for large language models (LLMs) often reduce culture to single-turn factual recall via MCQs, failing to capture a common use case: users seeking pract…
Skin-Deep: A Geometric Diagnostic for Alignment Fragility in Large Language Model Representations
Dongyub Jude Lee, Jungseob Lee, Seungyoon Lee +5
Refusal on a safety benchmark does not reveal how stable that behavior will remain after model updates. Benign downstream fine-tuning can weaken refusal, yet behavioral evaluations…
DART: Draft-Agreement Routing for Training-Free Adaptive Thinking Budgets in Hybrid Reasoning Models
Jungseob Lee, Seongtae Hong, Seungjun Lee +7
Hybrid reasoning models can answer directly or spend extra tokens on extended thinking. A practical router should choose between these modes for each query, so easy problems avoid…
No Reader Left Behind: Multi-Agent Summaries Everyone Can Understand
Jimin Jung, MyoungJin Kim, Jaehyung Seo +1
The Plain Writing Act in the United States requires government documents to be accessible in clear and simple language that the general public can easily understand, yet existing s…
HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering
Joongmin Shin, Gyuho Shim, Jeongbae Park +2
Retrieval-augmented generation (RAG) for document-based Open-domain Question Answering (ODQA) on large-scale industrial corpora faces two critical bottlenecks: routing failure in l…
M3DocDep: Multi-modal, Multi-page, Multi-document Dependency Chunking with Large Vision-Language Models
Joongmin Shin, Jeongbae Park, Jaehyung Seo +1
In long, multi-page industrial documents, retrieval-augmented generation (RAG) depends heavily on whether chunk boundaries follow the document's true structure. Existing text-centr…
MultiDocFusion: Hierarchical and Multimodal Chunking Pipeline for Enhanced RAG on Long Industrial Documents
Joongmin Shin, Chanjun Park, Jeongbae Park +2
RAG-based QA has emerged as a powerful method for processing long industrial documents. However, conventional text chunking approaches often neglect complex and long industrial doc…
The Impact of Negated Text on Hallucination with Large Language Models
Jaehyung Seo, Hyeonseok Moon, Heuiseok Lim
Recent studies on hallucination in large language models (LLMs) have been actively progressing in natural language processing. However, the impact of negated text on hallucination…
Metric Calculating Benchmark: Code-Verifiable Complicate Instruction Following Benchmark for Large Language Models
Hyeonseok Moon, Seongtae Hong, Jaehyung Seo +1
Recent frontier-level LLMs have saturated many previously difficult benchmarks, leaving little room for further differentiation. This progress highlights the need for challenging b…
MMA-ASIA: A Multilingual and Multimodal Alignment Framework for Culturally-Grounded Evaluation
Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty +32
Large language models (LLMs) are now used worldwide, yet their multimodal understanding and reasoning often degrade outside Western, high-resource settings. We propose MMA-ASIA, a…
Call for Rigor in Reporting Quality of Instruction Tuning Data
Hyeonseok Moon, Jaehyung Seo, Heuiseok Lim
Instruction tuning is crucial for adapting large language models (LLMs) to align with user intentions. Numerous studies emphasize the significance of the quality of instruction tun…
CoME: An Unlearning-based Approach to Conflict-free Model Editing
Dahyun Jung, Jaehyung Seo, Jaewook Lee +2
Large language models (LLMs) often retain outdated or incorrect information from pre-training, which undermines their reliability. While model editing methods have been developed t…
Find the Intention of Instruction: Comprehensive Evaluation of Instruction Understanding for Large Language Models
Hyeonseok Moon, Jaehyung Seo, Seungyoon Lee +2
One of the key strengths of Large Language Models (LLMs) is their ability to interact with humans by generating appropriate responses to given instructions. This ability, known as…
Toward Practical Automatic Speech Recognition and Post-Processing: a Call for Explainable Error Benchmark Guideline
Seonmin Koo, Chanjun Park, Jinsung Kim +4
Automatic speech recognition (ASR) outcomes serve as input for downstream tasks, substantially impacting the satisfaction level of end-users. Hence, the diagnosis and enhancement o…
Knowledge Graph-Augmented Korean Generative Commonsense Reasoning
Dahyun Jung, Jaehyung Seo, Jaewook Lee +2
Generative commonsense reasoning refers to the task of generating acceptable and logical assumptions about everyday situations based on commonsense understanding. By utilizing an e…
Synthetic Alone: Exploring the Dark Side of Synthetic Data for Grammatical Error Correction
Chanjun Park, Seonmin Koo, Seolhwa Lee +4
Data-centric AI approach aims to enhance the model performance without modifying the model and has been shown to impact model performance positively. While recent attention has bee…
Self-Improving-Leaderboard(SIL): A Call for Real-World Centric Natural Language Processing Leaderboards
Chanjun Park, Hyeonseok Moon, Seolhwa Lee +3
Leaderboard systems allow researchers to objectively evaluate Natural Language Processing (NLP) models and are typically used to identify models that exhibit superior performance o…
Language Chameleon: Transformation analysis between languages using Cross-lingual Post-training based on Pre-trained language models
Suhyune Son, Chanjun Park, Jungseob Lee +5
As pre-trained language models become more resource-demanding, the inequality between resource-rich languages such as English and resource-scarce languages is worsening. This can b…