SEEval: Advancing LLM Text Evaluation Efficiency and Accuracy through Self-Explanation Prompting (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have achieved remarkable success in various natural language generation tasks, but their performance in automatic text evaluation is not ready as human replacements. |
| Approach: | They propose a prompt-based text evaluator that incorporates self-explanation, a metacognitive strategy, to enhance automatic text evaluation. |
| Outcome: | The proposed method achieves competitive and often superior performance compared to the two state-of-the-art baselines – G-Eval and Analyze-Rate – and is 20 times more efficient in terms of run-time. |
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