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Found 6 papers, 1 papers with code
Toward Exploring the Code Understanding Capabilities of Pre-trained Code Generation Models
While these models acquire vast amounts of code knowledge, they perform poorly on code understanding tasks, such as code search and clone detection, as they are specifically trained for generation.
AcademicGPT: Empowering Academic Research
Our training corpus mainly consists of academic papers, thesis, content from some academic domain, high-quality Chinese data and others.
Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation
Meanwhile, while traditional techniques leveraging such semantic information require complex static or dynamic code analysis to obtain features such as data flow and control flow, SeCoT demonstrates that this process can be fully automated via the intrinsic capabilities of LLMs (i. e., in-context learning), while being generalizable and applicable to challenging domains.
Towards Effective and Compact Contextual Representation for Conformer Transducer Speech Recognition Systems
Current ASR systems are mainly trained and evaluated at the utterance level.
BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
Furthermore, we conduct ablation studies on the pretraining tasks for BioBART and find that sentence permutation has negative effects on downstream tasks.
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BIOS: An Algorithmically Generated Biomedical Knowledge Graph
For decades, these knowledge graphs have been developed via expert curation; however, this method can no longer keep up with today's AI development, and a transition to algorithmically generated BioMedKGs is necessary.
