Papers by Shichang Zhang
BizCompass: Benchmarking the Reasoning Capabilities of LLMs in Business Knowledge and Applications (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks focus on narrow tasks and leave a fundamental question unanswered . Existing models only focus on specific tasks, requiring rigorous reasoning and knowledge . |
| Approach: | They propose a benchmark to connect theoretical foundations with practical business knowledge and applications. |
| Outcome: | The benchmark systematically evaluates both open-source and commercial LLMs . it reveals how theoretical knowledge translates into practical performance in business . |
Automated Molecular Concept Generation and Labeling with Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Concept-based models lack explainability and need predefined concepts and manual labeling in molecular science. |
| Approach: | They propose a framework that leverages Large Language Models to generate and label predictive molecular concepts without human input. |
| Outcome: | The proposed framework outperforms existing models on several benchmarks while maintaining explainability and allowing easy intervention. |
FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing work models taxonomy concepts as vectors or geometric objects, but fuzzy sets are efficient for concept modeling. |
| Approach: | They propose a set representation learning task based on fuzzy set approximation . they demonstrate remarkable improvements in taxonomy expansion using FUSE . |
| Outcome: | The proposed framework improves taxonomy expansion performance by 23% over baselines. |