| Challenge: | Recent studies have employed large language models (LLMs) as reference-free metrics for NLG evaluation, enhancing adaptability to new tasks tasks. |
| Approach: | They propose a method that leverages large language models to integrate insights from various assistant evaluators. |
| Outcome: | The proposed approach achieves a 0.962 system-level Kendall-Tau correlation with humans on SummEval and a 0.7444 turn-level Spearman correlation on TopicalChat, which is significantly higher than baseline methods. |
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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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Co-Eval: Augmenting LLM-based Evaluation with Machine Metrics (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have emerged as key drivers of progress in the field of natural language processing. |
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Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? (2024.findings-eacl)
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Rishav Hada, Varun Gumma, Adrian Wynter, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali, Sunayana Sitaram
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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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| Challenge: | Conventional reference-based metrics have low correlation with human judgments, especially for open-ended generation tasks. |
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| Challenge: | Large Language Models (LLMs) are scalable and economical evaluators, but how reliable they are is still under-explored. |
| Approach: | They propose a framework which breaks down the evaluation process into decomposition and aggregation stages based on pedagogical practices and provides an interpretable window for how well LLMs evaluate . |
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Themis: A Reference-free NLG Evaluation Language Model with Flexibility and Interpretability (2024.emnlp-main)
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| Challenge: | Existing methods for evaluation of natural language generation tasks lack reliable data. |
| Approach: | They propose to use annotations from human and GPT-4 to construct a corpus for NLG evaluation. |
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