Aug 8, 2021 · In this work, we study different evaluation metrics that have been proposed to evaluate quality, diversity and consistency of machine-generated text.
This work studies different evaluation metrics that have been proposed to evaluate quality, diversity and consistency of machine-generated text, ...
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Sep 11, 2024 · Based on this finding,. we argue that Corpus-BLEU is the best metric to evaluate the quality of generated text,. since it behaves more like human ...
The decoding algorithm is critical for open-ended text generation, transforming latent representations into coherent and meaningful outputs. This paper ...
Abstract. Recent research has increasingly focused on evaluating large language models' (LLMs) alignment with diverse human values and preferences ...
[PDF] A Learned Metric for Open-ended Text Generation Evaluation
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We propose a learned evalu- ation metric: Perception Score. It utilizes a pre-trained model and considers context information for conditional generation.
Oct 31, 2022 · In this work, we measure and improve the factual accuracy of large-scale LMs for open-ended text generation. We design the FactualityPrompts ...
Abstract. Pretrained language models (PLMs), especially large language models (LLMs) demonstrate impressive capabilities in open-ended text generation.
Our goal is to automatically measure and evaluate the factuality of large-scale pre-trained language models (LMs) for open-ended text generation. Factuality ...
Open-eNded Language Generation (ONLG) refers to those generation tasks where only very limited information is given in the input and there are many ...