Papers by Haozhen Zhang
Learning More from Less: Exploiting Counterfactuals for Data-Efficient Chart Understanding (2026.acl-long)
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Jianzhu Bao, Haozhen Zhang, Kuicai Dong, Bozhi Wu, Sarthak Ketanbhai Modi, Zi Pong Lim, Yon Shin Teo, Wenya Wang
| Challenge: | Chart understanding is a critical capability for vision-language models, serving as a cornerstone for automated data analysis, document understanding, and scientific research. |
| Approach: | They propose a chart-efficient training framework to enhance counterfactual sensitivity by code modification and a similarity-based data selection strategy. |
| Outcome: | The proposed framework achieves superior or comparable performance to strong chart-specific VLMs while using significantly less training data. |
Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs (2025.acl-long)
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| Challenge: | Existing methods for retrieving historical LLM responses are lacking in long-context summarization tasks. |
| Approach: | They propose a graph of records which leverages historical LLM responses to enhance RAG for long-context global summarization. |
| Outcome: | The proposed method improves on four long-context summarization datasets. |
Cache-of-Thought: Master-Apprentice Framework for Cost-Effective Vision Language Model Reasoning (2025.emnlp-main)
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Mingyuan Wu, Jize Jiang, Haozhen Zheng, Meitang Li, Zhaoheng Li, Beitong Tian, Bo Chen, Yongjoo Park, Minjia Zhang, ChengXiang Zhai, Klara Nahrstedt
| Challenge: | Recent Vision Language Models (VLMs) have shown tremendous promise in a wide range of realworld applications, but their size has made at-scale deployment and operation challenging due to high consumption of cloud computing resource, high latency, and expensive API calls. |
| Approach: | They propose a master–apprentice framework for collaborative inference between large and small vision language models. |
| Outcome: | The proposed framework improves reasoning performance on widely-recognized and challenging general reasoning benchmarks and specifically boosts reasoning of apprentice VLMs by 36.6%. |