17 papers
MixLID: Token-Level Language Identification in Mixed-Language Text
Seongtae Hong, Youngjoon Jang, Seungyoon Lee +2
Processing multilingual text requires both identifying a broad range of languages and distinguishing the language of each token in inputs containing multiple languages. We introduc…
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…
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…
Language Chain in Alignment: Cross-lingual Ranking Preference Optimization
Seungyoon Lee, Minhyuk Kim, Jungseob Lee +1
The alignment of Large Language Models heavily relies on English-centric high-quality preference data, which often leads to suboptimal performance in other languages. In this paper…
LAMAR: An Open Language-Aware Multilingual Alignment Reranker
Seongtae Hong, Youngjoon Jang, Jungseob Lee +2
In multilingual retrieval augmented generation pipelines, an embedding model can retrieve relevant documents written in multiple languages, which are subsequently reranked before a…
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…
Beyond Penalizing Mistakes: Stabilizing Efficiency Training in Large Reasoning Models via Adaptive Correct-Only Rewards
Jungseob Lee, Seungyoon Lee, Seongtae Hong +3
Training large language models to reason efficiently is a critical challenge. While integrating length-penalizing rewards into Group Relative Policy Optimization (GRPO) aims to red…
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…
CLEAR: Cross-Lingual Enhancement in Alignment via Reverse-training
Seungyoon Lee, Minhyuk Kim, Seongtae Hong +3
Existing multilingual embedding models often encounter challenges in cross-lingual scenarios due to imbalanced linguistic resources and less consideration of cross-lingual alignmen…
Parallel Test-Time Scaling with Multi-Sequence Verifiers
Yegon Kim, Seungyoo Lee, Chaeyun Jang +2
Parallel test-time scaling, which generates multiple candidate solutions for a single problem, is a powerful technique for improving large language model performance. However, it i…
TORSO: Template-Oriented Reasoning Towards General Tasks
Minhyuk Kim, Seungyoon Lee, Heuiseok Lim
The approaches that guide Large Language Models (LLMs) to emulate human reasoning during response generation have emerged as an effective method for enabling them to solve complex…
Improving Korean-English Cross-Lingual Retrieval: A Data-Centric Study of Language Composition and Model Merging
Youngjoon Jang, Junyoung Son, Taemin Lee +5
With the increasing utilization of multilingual text information, Cross-Lingual Information Retrieval (CLIR) has become a crucial research area. However, the impact of training dat…
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,…
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…
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…
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…
Alternative Speech: Complementary Method to Counter-Narrative for Better Discourse
Seungyoon Lee, Dahyun Jung, Chanjun Park +2
We introduce the concept of "Alternative Speech" as a new way to directly combat hate speech and complement the limitations of counter-narrative. An alternative speech provides pra…