Papers by Hwan Chang
How Do Large Vision-Language Models See Text in Image? Unveiling the Distinctive Role of OCR Heads (2025.emnlp-main)
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| Challenge: | Despite advances in Large Vision Language Models, a gap remains in their interpretability and performance. |
| Approach: | They identify the Optical Character Recognition Head (OCR Head) heads that are more efficient at recognizing text from images. |
| Outcome: | The Optical Character Recognition Head (OCR Head) is identified as the most efficient head for recognizing text from images. |
Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval (2025.findings-naacl)
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| Challenge: | Existing methods to optimize retrieve-and-generate processes for real-world scenarios may not be optimal for large language models. |
| Approach: | They propose a Probing-RAG which utilizes hidden state representations from the intermediate layers of language models to adaptively determine the necessity of additional retrievals for a given query. |
| Outcome: | The proposed method outperforms previous methods while reducing the number of redundant retrieval steps. |
Keep Security! Benchmarking Security Policy Preservation in Large Language Model Contexts Against Indirect Attacks in Question Answering (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly deployed in sensitive domains . large-scale benchmarks for contextual security preservation against attacks remain lacking . |
| Approach: | They evaluate 10 Large Language Models on a benchmark dataset to assess their adherence to contextual non-disclosure policies. |
| Outcome: | The proposed model fails to adhere to user-defined security policies in question answering . the model fails in indirect attacks, especially when it violates user-definable policies . |
Which Retain Set Matters for LLM Unlearning? A Case Study on Entity Unlearning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are prone to retaining unauthorized or sensitive information from their training data, which raises privacy concerns. |
| Approach: | They propose to use a group of queries that share similar syntactic structures with the data targeted for removal to investigate the effects of unlearning on various subsets of the retain set. |
| Outcome: | The proposed method reduces the retention set, the portion of training data that is not targeted for removal, and improves model performance across subsets. |
Doc-PP: Document Policy Preservation Benchmark for Large Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing safety research focuses on implicit social norms or text-only settings, overlooking the complexities of multimodal documents. |
| Approach: | They propose a benchmark to assess the safety of large vision-Language Models (LVLMs) they propose 'Document Policy Preservation Benchmark' to assess document policy compliance. |
| Outcome: | The proposed framework outperforms standard prompting defenses in the evaluation of multimodal documents. |