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Found 6 papers, 1 papers with code
From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications
Therefore, our study shifts the focus from model-centered to human-centered evaluation in the context of AI-powered writing assistance applications.
Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning
We advocate the few-shot in-context curriculum learning (ICCL), a simple but effective demonstration ordering method for ICL, which implies gradually increasing the complexity of prompt demonstrations during the inference process.
Know Where to Go: Make LLM a Relevant, Responsible, and Trustworthy Searcher
The advent of Large Language Models (LLMs) has shown the potential to improve relevance and provide direct answers in web searches.
Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge
Inspired by recent advancement in prompt learning, in this paper, we propose the Mix Prompt Tuning (MPT), which is a semi-supervised method to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks with a small number of labeled examples.
AI vs. Human -- Differentiation Analysis of Scientific Content Generation
We find that there exists a "writing style" gap between AI-generated scientific text and human-written scientific text.
Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network
We extracted ninety-one paper features from three dimensions as the input of the model, including twenty-one features in the paper dimension, thirty-five in the reference dimension, and thirty-five in the citing paper dimension.
