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The Eval4NLP 2023 Shared Task on Prompting Large Language Models as Explainable Metrics.

, , , , , and . Eval4NLP, page 117-138. Association for Computational Linguistics, (2023)

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Understanding the Extent to which Summarization Evaluation Metrics Measure the Information Quality of Summaries., and . CoRR, (2020)A Needle in a Haystack: An Analysis of High-Agreement Workers on MTurk for Summarization., , , , , , , , , and 1 other author(s). ACL (1), page 14944-14982. Association for Computational Linguistics, (2023)Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary., , and . CoRR, (2020)Pinpoint, Not Criticize: Refining Large Language Models via Fine-Grained Actionable Feedback., , , , , , , , and . CoRR, (2023)GEMv2: Multilingual NLG Benchmarking in a Single Line of Code., , , , , , , , , and 66 other author(s). CoRR, (2022)Ties Matter: Meta-Evaluating Modern Metrics with Pairwise Accuracy and Tie Calibration., , and . EMNLP, page 12914-12929. Association for Computational Linguistics, (2023)On the Limitations of Reference-Free Evaluations of Generated Text., , and . EMNLP, page 10960-10977. Association for Computational Linguistics, (2022)Benchmarking Answer Verification Methods for Question Answering-Based Summarization Evaluation Metrics., and . ACL (Findings), page 3759-3765. Association for Computational Linguistics, (2022)Training and Meta-Evaluating Machine Translation Evaluation Metrics at the Paragraph Level., , , and . WMT, page 996-1013. Association for Computational Linguistics, (2023)Quality Estimation Using Minimum Bayes Risk., , and . WMT, page 806-811. Association for Computational Linguistics, (2023)