CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists (2025.emnlp-main)
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| Challenge: | Existing evaluation protocols for text generation suffer from rating inconsistencies . lexical overlap-based metrics align poorly with human judgments . |
| Approach: | They propose a checklist-based evaluation framework that improves rating reliability via decomposed binary questions. |
| Outcome: | The proposed framework improves rating reliability by decomposing binary questions . it improves agreement across evaluator models by 0.45 and reduces score variance . human evaluation remains the gold standard, but it #, Equal contribution. |
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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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FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models (2024.emnlp-demo)
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LLM as a Meta-Judge: Synthetic Data for NLP Evaluation Metric Validation (2026.acl-srw)
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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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Krithika Ramesh, Daniel Smolyak, Zihao Zhao, Nupoor Gandhi, Ritu Agarwal, Margrét V. Bjarnadóttir, Anjalie Field
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| Challenge: | AutoChecklist is an open-source library that unifies checklist-based evaluation into composable pipelines. |
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| Challenge: | Large language models (LLMs) are prone to inconsistencies and individual biases, limiting their reliability. |
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| Challenge: | Existing frameworks for LLM-as-a-judge use zero-shot setting without consulting any human input, which leads to low alignment, or fine-tune LLMs on labeled data, which requires a non-trivial number of samples. |
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RepEval: Effective Text Evaluation with LLM Representation (2024.emnlp-main)
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Shuqian Sheng, Yi Xu, Tianhang Zhang, Zanwei Shen, Luoyi Fu, Jiaxin Ding, Lei Zhou, Xiaoying Gan, Xinbing Wang, Chenghu Zhou
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