Papers with AGIEval
SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models (2025.naacl-industry)
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| Challenge: | Typical evaluations of Large Language Models (LLMs) report a single accuracy metric per dataset, often derived from an optimized setup. |
| Approach: | They propose a framework for non-adversarial evaluation of large language models that evaluates models by repeatedly testing them on the same benchmarks in various setups. |
| Outcome: | The proposed framework evaluates models by repeatedly testing them on the same benchmarks in various setups to give a realistic estimate of their accuracy and consistency. |
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. |
Lion: Adversarial Distillation of Proprietary Large Language Models (2023.emnlp-main)
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| Challenge: | Existing methods for transferring knowledge from a proprietary large language model to an open-source LLM have been criticized for not incorporating any feedback to boost student model's proficiency iteratively. |
| Approach: | They propose a novel adversarial distillation framework that integrates feedback to identify challenging instructions where the student model’s performance falls short. |
| Outcome: | The proposed framework surpasses existing state-of-the-art LLMs by 55.4% in challenging zero-shot reasoning benchmarks and 16.7% on AGIEval. |
DFPE: A Diverse Fingerprint Ensemble for Enhancing LLM Performance (2026.findings-eacl)
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| Challenge: | Large Language Models (LLMs) exhibit inconsistent performance across diverse domains. |
| Approach: | They propose a method that systematically constructs subject-adaptive ensembles by balancing model diversity and competence. |
| Outcome: | The proposed method achieves 17.1% gain over the best single model, reaching 71.4% accuracy on the MMLU-pro benchmark. |