

ASPIRE
Overview :
ASPIRE is a well-designed framework designed to boost the selective prediction capability of large language models. It leverages parameter-efficient fine-tuning training to enable LLMs to self-assess and provide confidence scores for generated answers. Experimental results indicate that ASPIRE significantly outperforms current selective prediction methods on various question-answering datasets.
Target Users :
["Enhance the reliability of question-answering systems","Reduce the uncertainty of language models in critical decision-making"]
Use Cases
ASPIRE can enhance the ability of chatbots to judge the correctness of their own answers.
ASPIRE enables language models to confidently provide answers only when certain of the correct answer, reducing erroneous predictions.
ASPIRE improves the ability of question-answering models to judge the accuracy of answers.
Features
Perform task-specific parameter adjustments to improve model performance
Generate potential candidate answers
Learn self-assessment to differentiate between correct and incorrect answers
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