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Found 7 papers, 6 papers with code
Large Language Model-Based Agents for Software Engineering: A Survey
The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i. e., LLM-based agents.
A Refer-and-Ground Multimodal Large Language Model for Biomedicine
Additionally, we introduce a Refer-and-Ground Multimodal Large Language Model for Biomedicine (BiRD) by using this dataset and multi-task instruction learning.
SynFundus-1M: A High-quality Million-scale Synthetic fundus images Dataset with Fifteen Types of Annotation
Large-scale public datasets with high-quality annotations are rarely available for intelligent medical imaging research, due to data privacy concerns and the cost of annotations.
ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation
Third, we find that generating the entire class all at once (i. e. holistic generation strategy) is the best generation strategy only for GPT-4 and GPT-3. 5, while method-by-method generation (i. e. incremental and compositional) is better strategies for the other models with limited ability of understanding long instructions and utilizing the middle information.
Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation
First, for the zero-shot setting, instructed LLMs are very competitive on code comprehension and generation tasks and sometimes even better than small SOTA models specifically fine-tuned on each downstream task.
Fast Online Hashing with Multi-Label Projection
When a new query arrives, only the binary codes of the corresponding potential neighbors are updated.
Robust Retinal Vessel Segmentation from a Data Augmentation Perspective
In this paper, we propose two new data augmentation modules, namely, channel-wise random Gamma correction and channel-wise random vessel augmentation.
