Papers with meta-evaluation
Analyzing and Evaluating Correlation Measures in NLG Meta-Evaluation (2025.naacl-long)
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| Challenge: | Existing studies have not investigated the differences between different correlation measures in meta-evaluation. |
| Approach: | They analyze 12 common correlation measures using real-world data from six widely-used NLG evaluation datasets and 32 evaluation metrics. |
| Outcome: | The proposed measures exhibit the best performance in discriminative power and ranking consistency . the measures using system-level grouping or Kendall correlation are the least sensitive to score granularity . |
METAL: Towards Multilingual Meta-Evaluation (2024.findings-naacl)
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| Challenge: | Recent studies show that Large Language Models excel on many standard NLP benchmarks. |
| Approach: | They propose a framework for end-to-end evaluation of Large Language Models as evaluators in multilingual scenarios. |
| Outcome: | The proposed framework evaluates LLMs as evaluators in multilingual scenarios. |
Accuracy is not enough: Evaluating Personalization in Summarizers (2023.findings-emnlp)
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| Challenge: | Existing accuracy measures cannot evaluate the degree of personalization of summarization models. |
| Approach: | They propose to use a PENS dataset to analyze the degree of personalization of ten different summarization models. |
| Outcome: | The proposed measure can evaluate the degree of personalization of summarization models using the PENS dataset. |
Word Embedding-Based Automatic MT Evaluation Metric using Word Position Information (N19-1)
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| Challenge: | Existing evaluation metrics for machine translation are difficult to address word meaning because it is a surface-level metric. |
| Approach: | They propose to use word embeddings, sentence-level tf-idf, and cosine similarity between two word embeds as features, weight, and the distance between two features as features. |
| Outcome: | The proposed metric can evaluate machine translation based on word meaning . it achieves highest correlation with human judgment among several representative metrics. |
Non-Repeatable Experiments and Non-Reproducible Results: The Reproducibility Crisis in Human Evaluation in NLP (2023.findings-acl)
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| Challenge: | reproducibility of human evaluations is rarely queried in NLP . authors estimate that just 5% of humanevaluations are repeatable . |
| Approach: | They propose to make human evaluations more repeatable and more reproducible . they estimate that just 5% of human evaluation experiments are repeatable . |
| Outcome: | The results show that human evaluations are rarely queried or formally tested in NLP . the authors estimate that just 5% of human evaluation experiments are repeatable . |
Automatic, Meta and Human Evaluation for Multimodal Summarization with Multimodal Output (2024.naacl-long)
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| Challenge: | Multimodal summarization with multimodal output (MSMO) has attracted increasing research interest . evaluation is an emerging yet underexplored research topic . |
| Approach: | They propose a framework that studies three research questions of MSMO evaluation . they propose an automatic evaluation metric and a meta-evaluation benchmark dataset . |
| Outcome: | The proposed evaluation metric and human-annotated meta-evaluation benchmark are used to assess the quality of evaluation metrics and show the framework is effective. |
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects (2024.naacl-long)
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| Challenge: | X-Eval is a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users. |
| Approach: | They introduce a two-stage instruction tuning framework to evaluate text in both seen and unseen aspects customized by end users. |
| Outcome: | The proposed framework improves the model’s ability to follow evaluation instructions and enhances the learning stage to better assess text quality. |
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction (2022.lrec-1)
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Daisuke Suzuki, Yujin Takahashi, Ikumi Yamashita, Taichi Aida, Tosho Hirasawa, Michitaka Nakatsuji, Masato Mita, Mamoru Komachi
| Challenge: | Existing studies on automatic evaluation of grammatical error correction (GEC) have shown that quality estimation models built from manual evaluation can achieve high performance in automatic evaluation in English. |
| Approach: | They used a dataset with manual evaluation to build an automatic evaluation model for Japanese GEC. |
| Outcome: | The proposed model is based on a Japanese dataset with manual evaluation and meta-evaluation. |
Rethinking Efficient Multilingual Text Summarization Meta-Evaluation (2024.findings-acl)
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| Challenge: | a limited number of human annotations are required to evaluate multilingual summarization evaluation metrics. |
| Approach: | They propose a multilingual meta-evaluation framework that uses machine translation systems to transform a monolingual metaevaluations dataset into multilingual versions. |
| Outcome: | The proposed framework outperforms classical text-matching-based metrics in non-English languages. |
Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification (2026.findings-acl)
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Yuxuan Wan, Tianqing Fang, Zaitang LI, Yintong Huo, Wenxuan Wang, Haitao Mi, Dong Yu, Michael R. Lyu
| Challenge: | Recent advances in Deep Research Agents (DRAs) are transforming automated knowledge discovery and problem-solving. |
| Approach: | They propose an inference-time scaling of verification wherein an agent self-improves at test time by evaluating its generated answers. |
| Outcome: | The proposed model outperforms vanilla agent-as-judge and LLM judge baselines by 12%–48% in meta-evaluation F1 score. |
A Dual-Perspective NLG Meta-Evaluation Framework with Automatic Benchmark and Better Interpretability (2025.acl-long)
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| Challenge: | Existing evaluation metrics are insufficient to meet requirements for natural language generation. |
| Approach: | They propose a dual-perspective NLG meta-evaluation framework that focuses on different evaluation capabilities and a method of automatically constructing benchmarks without requiring new human annotations. |
| Outcome: | The proposed framework improves interpretability and provides better performance for 16 representative LLMs. |