Papers by Jiho Park
SuperST: Superficial Self-Training for Few-Shot Text Classification (2024.lrec-main)
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| Challenge: | In few-shot text classification, self-training relies on pseudo-labels to expand data, which has shown success, but can accumulate errors due to noisy pseudo-labeled data. |
| Approach: | They propose a method to mitigate noise in noisy pseudo-labeled data by applying superficial learning to noisy data and fine-tuning to less noisy data. |
| Outcome: | The proposed framework improves the classifier accuracy for few-shot text classification by 18.5% at most and 8% in average, compared with the state-of-the-art SSL baselines. |
FactKG: Fact Verification via Reasoning on Knowledge Graphs (2023.acl-long)
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| Challenge: | knowledge graphs (KGs) have not been fully utilized as a knowledge source for fact verification. |
| Approach: | They propose a dataset to enable the community to better use knowledge graphs . they propose 108k natural language claims with five types of reasoning . |
| Outcome: | The proposed dataset consists of 108k natural language claims with five types of reasoning . authors believe the proposed method can advance reliability and practicality . |
PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation (2026.acl-long)
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| Challenge: | Existing methods for scientific poster generation lack hierarchical document understanding and coherent content-layout planning. |
| Approach: | They propose a training-free framework for scientific poster generation that captures document hierarchy and semantics across multiple levels. |
| Outcome: | The proposed framework outperforms existing methods in both automatic and human evaluations without additional training or domain-specific supervision. |
3D-Aware Vision-Language Models Fine-Tuning with Geometric Distillation (2025.findings-emnlp)
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| Challenge: | Vision-Language Models (VLMs) have shown remarkable performance on diverse visual and linguistic tasks, yet they remain limited in their understanding of 3D spatial structures. |
| Approach: | They propose a framework that injects human-inspired geometric cues into pretrained VLMs . they use sparse correspondences, relative depth relations and dense cost volumes . |
| Outcome: | The proposed framework outperforms existing methods on vision-language reasoning and 3D perception benchmarks. |
TRUEBench: Can LLM Response Meet Real-world Constraints as Productivity Assistant? (2025.findings-emnlp)
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| Challenge: | Existing benchmarks fail to evaluate large language models' instruction-following capabilities . current benchmarks lack multilinguality, implicit constraints and multi-turn dialogue . |
| Approach: | a new benchmark is designed to evaluate large language models' instruction-following capabilities . the benchmark features input prompts across 12 languages and includes inter-instance multilingual instructions . |
| Outcome: | a new benchmark for large language models (LLMs) is designed to assess their performance in real-world settings. |