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Found 12 papers, 5 papers with code
A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis
To bridge the distribution gap between user input behavior and model training datasets, we first construct a novel Coarse-Fine Granularity Prompts dataset (CFP) and propose a novel User-Friendly Fine-Grained Text Generation framework (UF-FGTG) for automated prompt optimization.
KADEL: Knowledge-Aware Denoising Learning for Commit Message Generation
However, previous methods are trained on the entire dataset without considering the fact that a portion of commit messages adhere to good practice (i. e., good-practice commits), while the rest do not.
Retrieval-Augmented Generation for Large Language Models: A Survey
Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.
Plug-and-Play Feature Generation for Few-Shot Medical Image Classification
Finally, the generated features and prototypes are together to train a more generalized classifier.
Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks.
ReCo: A Dataset for Residential Community Layout Planning
In order to address the issues and advance a benchmark dataset for various intelligent spatial design and analysis applications in the development of smart city, we introduce Residential Community Layout Planning (ReCo) Dataset, which is the first and largest open-source vector dataset related to real-world community to date.
Prompt-based Generative Approach towards Multi-Hierarchical Medical Dialogue State Tracking
The dialogue state tracking (DST) module in the medical dialogue system which interprets utterances into the machine-readable structure for downstream tasks is particularly challenging.
Revealing Secrets in SPARQL Session Level
Based on Semantic Web technologies, knowledge graphs help users to discover information of interest by using live SPARQL services.
Adversarial Learning for Chinese NER from Crowd Annotations
To quickly obtain new labeled data, we can choose crowdsourcing as an alternative way at lower cost in a short time.
Chinese Named Entity Recognition
named-entity-recognition
+2
SMR: Medical Knowledge Graph Embedding for Safe Medicine Recommendation
To our best knowledge, SMR is the first to learn embeddings of a patient-disease-medicine graph for medicine recommendation in the world.
A Graph Traversal Based Approach to Answer Non-Aggregation Questions Over DBpedia
We present a question answering system over DBpedia, filling the gap between user information needs expressed in natural language and a structured query interface expressed in SPARQL over the underlying knowledge base (KB).
