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Found 5 papers, 3 papers with code
MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding
The results highlight the high difficulty of these tasks and the significant performance gap among models.
Unveiling the Impact of Coding Data Instruction Fine-Tuning on Large Language Models Reasoning
First, coding data tuning enhances the overall reasoning capabilities of LLMs across different model families and scales.
GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks
Automatically evaluating vision-language tasks is challenging, especially when it comes to reflecting human judgments due to limitations in accounting for fine-grained details.
AlpaCare:Instruction-tuned Large Language Models for Medical Application
Instruction-finetuning (IFT) has become crucial in aligning Large Language Models (LLMs) with diverse human needs and has shown great potential in medical applications.
Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting
Although offering improved data privacy protection, domain-specific small language models (SLMs) often underperform LLMs, emphasizing the need for methods that reduce this performance gap while alleviating privacy concerns.
