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Found 7 papers, 4 papers with code
Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers?
In this paper, we explore the hypothesis that LLMs process concepts of varying complexities in different layers, introducing the idea of "Concept Depth" to suggest that more complex concepts are typically acquired in deeper layers.
Goal-guided Generative Prompt Injection Attack on Large Language Models
Although there is currently a large amount of research on prompt injection attacks, most of these black-box attacks use heuristic strategies.
Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities
However, there is a research gap in the LLMs' preferences in this field.
Health-LLM: Personalized Retrieval-Augmented Disease Prediction System
Compared to traditional health management applications, our system has three main advantages: (1) It integrates health reports and medical knowledge into a large model to ask relevant questions to large language model for disease prediction; (2) It leverages a retrieval augmented generation (RAG) mechanism to enhance feature extraction; (3) It incorporates a semi-automated feature updating framework that can merge and delete features to improve accuracy of disease prediction.
The Impact of Reasoning Step Length on Large Language Models
Alternatively, shortening the reasoning steps, even while preserving the key information, significantly diminishes the reasoning abilities of models.
Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning
Generalized zero-shot learning (GZSL) aims to recognize samples from both seen and unseen classes using only seen class samples for training.
A Simple and Effective Baseline for Attentional Generative Adversarial Networks
Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task.
