6 citations · 23 across the 46 of their papers we have counts for
40 papers · 1 filter
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…
Faster Block-Diffusion Serving with Distribution-Free Risk Guarantees
Jungseob Lee, Dongyub Jude Lee, Chanjun Park +2
Block-diffusion language models are served at hand-picked operating points, such as acceptance thresholds, buffer depth, schedule, checkpoint and precision, and each point is chose…
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…
Answer-Conditioned Chains of Thought Degrade Verifiable-Reasoning Distillation in Large Language Models
Jungseob Lee, Seungyoon Lee, Suhyune Son +4
A standard recipe for distilling the reasoning ability of large language models (LLMs) is to sample chains of thought from the model, keep those that reach the correct final answer…
Unveiling the Limits of Large Language Models in Inferring Pragmatic Meaning from Non-Verbal Responses
Sugyeong Eo, Heuiseok Lim
Although large language models (LLMs) have shown considerable progress in pragmatic language understanding, prior research has focused mainly on their comprehension of verbal behav…
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…
LANGSAE EDITING: Improving Multilingual Information Retrieval via Post-hoc Language Identity Removal
Dongjun Kim, Jeongho Yoon, Chanjun Park +1
Dense retrieval in multilingual settings often searches over mixed-language collections, yet multilingual embeddings encode language identity alongside semantics. This language sig…
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…
KITE: A Benchmark for Evaluating Korean Instruction-Following Abilities in Large Language Models
Dongjun Kim, Chanhee Park, Chanjun Park +1
The instruction-following capabilities of large language models (LLMs) are pivotal for numerous applications, from conversational agents to complex reasoning systems. However, curr…
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…
Benchmark Profiling: Mechanistic Diagnosis of LLM Benchmarks
Dongjun Kim, Gyuho Shim, Yongchan Chun +3
Large Language Models are commonly judged by their scores on standard benchmarks, yet such scores often overstate real capability since they mask the mix of skills a task actually…
NeedleChain: Measuring Intact Context Comprehension Capability of Large Language Models
Hyeonseok Moon, Heuiseok Lim
Recent reports suggest that LLMs can handle increasingly long contexts. However, many existing benchmarks for context understanding embed substantial query-irrelevant content, whic…
From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems
Youngjoon Jang, Seongtae Hong, Junyoung Son +3
Retrieval-Augmented Generation (RAG) has emerged as a crucial framework in natural language processing (NLP), improving factual consistency and reducing hallucinations by integrati…
Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval
Yongchan Chun, Minhyuk Kim, Dongjun Kim +2
Automatic Term Extraction (ATE) identifies domain-specific expressions that are crucial for downstream tasks such as machine translation and information retrieval. Although large l…
Semantic Aware Linear Transfer by Recycling Pre-trained Language Models for Cross-lingual Transfer
Seungyoon Lee, Seongtae Hong, Hyeonseok Moon +1
Large Language Models (LLMs) increasingly incorporate multilingual capabilities, fueling the demand to transfer them into target language-specific models. However, most approaches,…
Cross-Lingual Optimization for Language Transfer in Large Language Models
Jungseob Lee, Seongtae Hong, Hyeonseok Moon +1
Adapting large language models to other languages typically employs supervised fine-tuning (SFT) as a standard approach. However, it often suffers from an overemphasis on English p…
MIRAGE: A Metric-Intensive Benchmark for Retrieval-Augmented Generation Evaluation
Chanhee Park, Hyeonseok Moon, Chanjun Park +1
Retrieval-Augmented Generation (RAG) has gained prominence as an effective method for enhancing the generative capabilities of Large Language Models (LLMs) through the incorporatio…
FLEX: A Benchmark for Evaluating Robustness of Fairness in Large Language Models
Dahyun Jung, Seungyoon Lee, Hyeonseok Moon +2
Recent advancements in Large Language Models (LLMs) have significantly enhanced interactions between users and models. These advancements concurrently underscore the need for rigor…
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…
Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations
Yoonna Jang, Suhyune Son, Jeongwoo Lee +6
Despite the striking advances in recent language generation performance, model-generated responses have suffered from the chronic problem of hallucinations that are either untrue o…
ChatLang-8: An LLM-Based Synthetic Data Generation Framework for Grammatical Error Correction
Jeiyoon Park, Chanjun Park, Heuiseok Lim
We explore and improve the capabilities of LLMs to generate data for grammatical error correction (GEC). When merely producing parallel sentences, their patterns are too simplistic…
CharacterGPT: A Persona Reconstruction Framework for Role-Playing Agents
Jeiyoon Park, Chanjun Park, Heuiseok Lim
The recent introduction of the Assistants API highlights its potential for large language models (LLMs) in role-playing agents (RPA). However, maintaining consistent character pers…
Translation of Multifaceted Data without Re-Training of Machine Translation Systems
Hyeonseok Moon, Seungyoon Lee, Seongtae Hong +3
Translating major language resources to build minor language resources becomes a widely-used approach. Particularly in translating complex data points composed of multiple componen…