Papers by Jiwoo Hong
Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning (2026.eacl-long)
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| Challenge: | Recent advances in reinforcement learning with verifiable rewards (RLVR) show that large language models enhance their reasoning abilities when trained with veriable signals. |
| Approach: | They propose a method for a problem-aware filtering system that maximizes learning efficiency by selecting tasks of intermediate difficulty. |
| Outcome: | The proposed model improves when trained with verifiable rewards, but training efficiency is bottleneck . the proposed model achieves +12% gains in less than half the training steps of standard GRPO . |
Evaluating the Consistency of LLM Evaluators (2025.coling-main)
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| Challenge: | Large language models (LLMs) have shown potential as general evaluators with the benefits of speed and cost. |
| Approach: | They conduct extensive studies on the two aspects of consistency in LLM evaluations, Self-Consistency (SC) and Inter-scale Consistency on different scoring scales and criterion granularity with open-source and proprietary models. |
| Outcome: | The results show that strong proprietary models are not necessarily consistent evaluators, highlighting the importance of considering consistency in assessing the capability of LLM evalueators. |
Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy (2023.findings-emnlp)
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| Challenge: | a new method to detect political bias in news articles overcomes this domain dependency . partisan bias exists in various social issues, including the 2016 presidential election . |
| Approach: | They propose a multi-head hierarchical attention model that encodes the structure of long documents through a diverse ensemble of attention heads. |
| Outcome: | The proposed model outperforms existing methods for detecting political bias in news articles. |
Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning (2025.acl-long)
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| Challenge: | Recent studies show that pre-training compute can improve multilingual performance, but is it effective for test-time scaling? |
| Approach: | They propose a multilingual math benchmark with competition-level problems in 55 languages . they propose "test-time scaling" which further lengthens the time it takes to scale . |
| Outcome: | The proposed methods fail to generalize robustly across languages, with no improvements in variance or consistency. |
ORPO: Monolithic Preference Optimization without Reference Model (2024.emnlp-main)
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| Challenge: | Pre-trained language models with vast training corpora have shown remarkable abilities in diverse natural language processing tasks. |
| Approach: | They propose a model-free monolithic odds ratio preference optimization algorithm, ORPO, to improve preference alignment. |
| Outcome: | The proposed algorithm outperforms state-of-the-art language models with more than 7B and 13B parameters on the ultrafeedback alone. |
Cross-lingual Transfer of Reward Models in Multilingual Alignment (2025.naacl-short)
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| Challenge: | Recent studies in reward modeling schemes are skewed towards English, limiting the applicability of RLHF in multilingual alignments. |
| Approach: | They investigate cross-lingual transfer of English RMs by representation shifts . they also analyze cross-linguistic transfer of RM through the representation shift . |
| Outcome: | The results show that English RMs can be transferred across languages by 34% . |
Stable Language Model Pre-training by Reducing Embedding Variability (2024.emnlp-main)
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| Challenge: | Stable pre-training is essential for achieving better-performing language models, but tracking pre-train stability is impractical due to high computational costs. |
| Approach: | They propose to use Token Embedding Variability as a proxy to estimate pre-training stability. |
| Outcome: | The proposed method improves stability and lowers perplexities even at deeper layer counts. |