Papers by Junwoo Park
Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling (2024.findings-emnlp)
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| Challenge: | Existing approaches for text-based event prediction are limited in quality due to dynamic nature of international relations and conflicting economic dynamics. |
| Approach: | They propose a novel dataset that leverages the advanced reasoning capabilities of large-language models to address these limitations. |
| Outcome: | The proposed dataset features high-quality scoring labels generated through advanced prompt modeling and rigorously validated by domain experts in political science. |
One-Shot is Enough: Consolidating Multi-Turn Attacks into Efficient Single-Turn Prompts for LLMs (2025.acl-long)
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| Challenge: | a novel framework for consolidating multi-turn adversarial “jailbreak” prompts into single-turn queries is presented in a journal of computational linguistics. |
| Approach: | They propose a framework for consolidating adversarial “jailbreak” prompts into single-turn queries. |
| Outcome: | The proposed framework outperforms the original multi-turn attacks by up to 17.5 % in absolute ASR . it reduces token usage by more than half on average, and provides a powerful tool for large-scale red-teaming . |
Reward-Weighted Sampling: Enhancing Non-Autoregressive Characteristics in Masked Diffusion LLMs (2025.emnlp-main)
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| Challenge: | Masked diffusion models (MDMs) are promising non-autoregressive alternatives for large language modeling. |
| Approach: | They propose a method that leverages an external reward model to provide a principled global signal during the iterative diffusion process. |
| Outcome: | The proposed method improves non-autoregressive generation orders and performance across evaluation metrics. |
Revisiting LLMs as Zero-Shot Time Series Forecasters: Small Noise Can Break Large Models (2025.acl-short)
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| Challenge: | Large Language Models (LLMs) have shown remarkable performance across diverse tasks without domain-specific training, fueling interest in their potential for time series forecasting. |
| Approach: | They evaluate the effectiveness of LLMs as zero-shot forecasters compared to state-of-the-art domain-specific models by encoding sequences directly within prompts. |
| Outcome: | The proposed models perform well across multiple domains while reducing the need for domain-specific training. |