Papers by Amin Saied
SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning (2026.findings-eacl)
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| Challenge: | Existing red-teaming frameworks do not cover all the risks associated with arbitrary black-box LLMs. |
| Approach: | They propose a generic red-teaming framework for arbitrary black-box LLM agents that iteratively constructs and refines model-based adversarial attacks based on the execution trajectories of former attempts. |
| Outcome: | The proposed model improves attack success rate by 100%, surpassing the 671B Deepseek-R1 model. |
AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models (2024.findings-naacl)
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Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, Nan Duan
| Challenge: | Traditional benchmarks for evaluating foundation models often fail to accurately represent their general abilities for human-centric tasks. |
| Approach: | They propose a bilingual benchmark to assess foundation models in the context of human-centric standardized exams such as college entrance exams, law school admission tests, and math competitions. |
| Outcome: | The proposed benchmark exceeds the average human performance on SAT, LSAT, and math competitions with 95% accuracy and 92.5% on the Chinese college entrance English exam. |