Google researchers have introduced a pre-trained scoring model called Cappy, aimed at improving the performance of large multitask language models. Cappy's architecture is based on RoBERTa and is pre-trained on a diverse collection of datasets to improve the performance and efficiency of multitask law master's models. By introducing the lightweight pre-trained scoring model Cappy, it addresses the challenge of effectively utilizing large language models in multitask scenarios. Researchers have proposed a data construction method that meets the diversity requirements of labels in pre-training data, generating a large and effective regression pre-training dataset.

Last year, 260 million packages went missing in the U.S., with one in four adults experiencing package theft in the past year. DeliveryDefense is an AI-driven scoring system that helps courier companies identify target addresses for theft. The higher the score, the greater the likelihood of successful delivery. Merchants can reroute items to UPS stores or other pickup locations.

Zhipu AI has launched the high-quality, low-cost scoring model CritiqueLLM. Traditional evaluation metrics such as BLEU and ROUGE lack an understanding of overall semantics. CritiqueLLM proposes an interpretable and scalable text quality evaluation model that outperforms other models across 8 common tasks. CritiqueLLM generates scores through methods such as user query augmentation, reference text evaluation data collection, non-reference text evaluation data rewriting, and model training.

["📊 Evaluation System of Large Models: The current evaluation system for large models has issues such as open-source datasets that can be manipulated, fairness problems arising from closed evaluation datasets, and evaluation metrics that are not sufficiently scientific and comprehensive.", "💡 Trend of Large Model Applications: The article mentions that large models have evolved from model-level development to innovation at the application level.", "🔎 Commercialization Issues of Large Models: For large model teams, achieving commercialization is far more important than rankings and parameters." ]