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Found 10 papers, 5 papers with code
Latent Feature Mining for Predictive Model Enhancement with Large Language Models
Predictive modeling often faces challenges due to limited data availability and quality, especially in domains where collected features are weakly correlated with outcomes and where additional feature collection is constrained by ethical or practical difficulties.
Control Large Language Models via Divide and Conquer
This paper investigates controllable generation for large language models (LLMs) with prompt-based control, focusing on Lexically Constrained Generation (LCG).
REFFLY: Melody-Constrained Lyrics Editing Model
Automatic melody-to-lyric generation aims to produce lyrics that align with a given melody.
Diffusion Transformer Model With Compact Prior for Low-dose PET Reconstruction
In this research, we propose a diffusion transformer model (DTM) guided by joint compact prior (JCP) to enhance the reconstruction quality of low-dose PET imaging.
Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering
Proteins are essential to life's processes, underpinning evolution and diversity.
PET Tracer Conversion among Brain PET via Variable Augmented Invertible Network
Meanwhile, the invertible network iteratively estimates the resultant DOPA PET data and compares it to the reference DOPA PET data.
DISC-FinLLM: A Chinese Financial Large Language Model based on Multiple Experts Fine-tuning
We propose Multiple Experts Fine-tuning Framework to build a financial large language model (LLM), DISC-FinLLM.
Synthetic CT Generation via Variant Invertible Network for All-digital Brain PET Attenuation Correction
Perceptual analysis and quantitative evaluations illustrate that the invertible network for PET AC outperforms other existing AC models, which demonstrates the potential of the proposed method and the feasibility of achieving brain PET AC without CT.
DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services
We propose DISC-LawLLM, an intelligent legal system utilizing large language models (LLMs) to provide a wide range of legal services.
Rapid Image Labeling via Neuro-Symbolic Learning
To address this challenge, we propose a neuro-symbolic approach called Rapid, which infers image labeling rules from a small amount of labeled data provided by domain experts and automatically labels unannotated data using the rules.
