Papers by Boyang Zhao

4 papers
SPHERE: Unveiling Spatial Blind Spots in Vision-Language Models Through Hierarchical Evaluation (2025.acl-long)

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Challenge: Current vision-language models lack multi-dimensional spatial reasoning capabilities for human-like understanding and applications.
Approach: They propose a hierarchical evaluation framework that probes models across increasing levels of complexity and integrates spatial, visual, and logical understanding.
Outcome: The proposed framework probes models across increasing levels of complexity, from basic skills to multi-skill integration and high-level reasoning that combines spatial, visual, and logical understanding.
What Are We Measuring When We Evaluate Large Vision-Language Models? An Analysis of Latent Factors and Biases (2024.naacl-long)

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Challenge: Vision-language models have broad competence that is difficult to evaluate . current evaluation benchmarks focus on only assessing one or a few capabilities .
Approach: They perform a large-scale transfer learning experiment to discover latent VL skills from data.
Outcome: The results suggest that factor analysis can identify reasonable yet surprising VL skill factors . the results contribute to the design of balanced and broad-coverage vision-language evaluation methods.
RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services (2025.emnlp-industry)

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Challenge: Social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform content management and interaction quality improvement.
Approach: They propose a domain-specific LLM to break the performance bottleneck of single-task baselines and establish a comprehensive foundation for social networking services.
Outcome: The proposed model achieves an average improvement of 14.02% across 8 major tasks and 7.56% in bilingual evaluation benchmark, compared with baseline models.
Semantic search with domain-specific word-embedding and production monitoring in Fintech (2020.coling-demos)

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Challenge: a novel system with domain-specific custom language models for accurate search terms expansion addresses several challenges faced in an industry-setting . many industry-grade search engines exist, but their accuracy can be sub-optimal due to domain specificity and terms provided by the users.
Approach: They propose an end-to-end information retrieval system with domain-specific custom language models for accurate search terms expansion.
Outcome: The proposed system is used in risk management and has wide applicability to other domains.

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