2 citations · 5 across the 32 of their papers we have counts for
38 papers
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…
Recovering Off-Policy Supervision for Speculative Decoding
Jungseob Lee, Chanjun Park, Sugyeong Eo +1
Block drafters for speculative decoding are commonly trained on corpora written by external models, where a single off-policy token invalidates supervision for all subsequent slots…
RenderRank: Learning to Rerank Text with Compressed Visual Tokens
Seongtae Hong, Youngjoon Jang, Jungseob Lee +2
Rendering document text as images allows vision-language models to encode documents as visual tokens, which can reduce input sequence length compared with text input. This reductio…
AgentHop: A Diagnostic Benchmark for Agentic Multi-Hop Scientific Question Answering
Chanhee Park, Jeongho Yoon, Sungbin Han +2
Agentic tasks require a large language model to interact with the world, navigating information and gathering evidence across multiple steps with restricted resources. Due to this…
Risk-Controlled Selective LLM Answering by Pricing Label-Free Checks
Dongyub Jude Lee, Jungseob Lee, Chanjun Park +2
Serving an answer from a large language model requires deciding when to abstain, yet a verifier's ranking accuracy alone does not determine the error rate among served answers. We…
Learning to Refer: Client-Resolved Generation for Privacy-Aware Language Models
Jeongho Yoon, Chanhee Park, Yongchan Chun +5
Cloud-based large language models (LLMs) require users to disclose plaintext data to service providers, creating privacy risks in sensitive domains. Existing privacy-preserving app…
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…
SHIFT: Semantic Harmonization via Index-side Feature Transformation for Multilingual Information Retrieval
Youngjoon Jang, Seongtae Hong, Hyeonseok Moon +1
With the rapid expansion of massive multilingual corpora, Multilingual Information Retrieval (MLIR) has emerged as a critical technology for global information access. MLIR enables…
SemBridge: Language Transfer in Sparse Encoders via Multilingual Semantic Bridges
Seongtae Hong, Youngjoon Jang, Jia-Heui Ju +2
Sparse encoders offer high-precision retrieval by representing term importance within a vocabulary space, yet their English-centric structures pose a critical impediment to languag…
Llamion Technical Report
Kisu Yang, Yoonna Jang, Hyeonseok Moon +6
We release Llamion, a family of 14B-parameter open-weight language models obtained by transforming Orion-14B into the standardized Llama-family architecture. The transformation is…
MLAIRE: Multilingual Language-Aware Information Retrieval Evaluation Protocal
Youngjoon Jang, Seongtae Hong, Hyeonseok Moon +1
Multilingual Information Retrieval is increasingly important in real-world search settings, where users issue queries over mixed-language corpora. Existing evaluations mainly rewar…
LegalMidm: Use-Case-Driven Legal Domain Specialization for Korean Large Language Model
Youngjoon Jang, Chanhee Park, Hyeonseok Moon +5
In recent years, the rapid proliferation of open-source large language models (LLMs) has spurred efforts to turn general-purpose models into domain specialists. However, many domai…
Beyond Hard Negatives: The Importance of Score Distribution in Knowledge Distillation for Dense Retrieval
Youngjoon Jang, Seongtae Hong, Hyeonseok Moon +1
Transferring knowledge from a cross-encoder teacher via Knowledge Distillation (KD) has become a standard paradigm for training retrieval models. While existing studies have largel…
Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation
Jeongho Yoon, Chanhee Park, Yongchan Chun +2
Current LLM-based services typically require users to submit raw text regardless of its sensitivity. While intuitive, such practice introduces substantial privacy risks, as unautho…
Improving Semantic Proximity in Information Retrieval through Cross-Lingual Alignment
Seongtae Hong, Youngjoon Jang, Jungseob Lee +2
With the increasing accessibility and utilization of multilingual documents, Cross-Lingual Information Retrieval (CLIR) has emerged as an important research area. Conventionally, C…
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…
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…
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…
Debate Only When Necessary: Adaptive Multiagent Collaboration for Efficient LLM Reasoning
Sugyeong Eo, Hyeonseok Moon, Evelyn Hayoon Zi +2
Multiagent collaboration has emerged as a promising framework for enhancing the reasoning capabilities of large language models (LLMs). Despite improvements in reasoning, the appro…
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…