Yicheng Wang, Jiayi Yuan, Yu-Neng Chuang, Zhuoer Wang, Yingchi Liu, Mark Cusick, Param Kulkarni, Zhengping Ji, Yasser Ibrahim, Xia Hu
| Challenge: | Large Language Models (LLMs) are increasingly serving as evaluators in Natural Language Generation (NLG) tasks. |
| Approach: | They propose a framework that measures the discernment of Large Language Models (LLMs) across diverse NLG tasks. |
| Outcome: | The proposed framework provides quantitative discernment scores for LLMs across four NLG tasks. |
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Rishav Hada, Varun Gumma, Adrian Wynter, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali, Sunayana Sitaram
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Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)
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| Challenge: | introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. |
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LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks (2025.acl-short)
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Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, Desmond Elliott, Raquel Fernández, Albert Gatt, Esam Ghaleb, Mario Giulianelli, Michael Hanna, Alexander Koller, Andre Martins, Philipp Mondorf, Vera Neplenbroek, Sandro Pezzelle, Barbara Plank, David Schlangen, Alessandro Suglia, Aditya K Surikuchi, Ece Takmaz, Alberto Testoni
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| Challenge: | Existing work uses large language models (LLMs) to evaluate natural language process tasks, but there are shortcomings in current LLMs. |
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In Benchmarks We Trust ... Or Not? (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape . established automatic evaluation metrics are poor surrogates, correlating weakly with human judgement. |
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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. |
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From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)
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Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu
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CodeJudgeBench: Benchmarking LLM-as-a-Judge for Coding Tasks (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are increasingly used to judge code, but their reliability remains poorly understood. |
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