6 papers
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…
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…
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…
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…
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…
Exploring Coding Spot: Understanding Parametric Contributions to LLM Coding Performance
Dongjun Kim, Minhyuk Kim, YongChan Chun +2
Large Language Models (LLMs) have demonstrated notable proficiency in both code generation and comprehension across multiple programming languages. However, the mechanisms underlyi…