Papers by Nurit Cohen Inger
Forget What You Know about LLMs Evaluations - LLMs are Like a Chameleon (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) excel on public benchmarks, but high scores may mask overreliance on dataset-specific surface cues rather than true language understanding. |
| Approach: | They propose a meta-evaluation framework that systematically rephrases benchmark inputs to detect overfitting. |
| Outcome: | The proposed framework detects performance degradation indicative of superficial pattern reliance on dataset-specific cues and distortion levels. |
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. |