mPLUG-PaperOwl: Scientific Diagram Analysis with the Multimodal Large Language Model; Anwen Hu et al Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models; Haoning Wu et al SPHINX: THE JOINT MIXING OF WEIGHTS, TASKS, AND VISUAL EMBEDDINGS FOR MULTI-MODAL LARGE L...
Details regarding all corpora used in this paper, data preparation and compression can be found in Supplementary Information: Corpora. In Supplementary Fig. 1, we visualize several important aspects of our database (also see Supplementary Table 4). Supplementary Fig. 1a shows that most corpora ...
However, rather than radical action to make change, the findings suggest that the White Paper presents an illusory carapace of change that conceals fundamental continuity. It reassures all of the commitment of government and audiences to change while sustaining education as fundamentally unchanged. ...
1 Background and Motivation Mathematical texts can be computerised in many ways that capture differing amounts of the mathematical meaning. At one end, there is document imaging, which captures the arrangement of black marks on paper, while at the other end there are proof assistants (e.g., ...
Fig. 1: Large language models (LLMs) in medicine. Full size image Data availability No datasets were created in the context of this work. Examples of LLM outputs are provided in theSupplementary Data. References Download references Acknowledgements ...
If you still suffer from insufficient memory, you can consider Q-LoRA (paper), which uses the quantized large language model and other techniques such as paged attention to allow even fewer memory costs. Note: to run single-GPU Q-LoRA training, you may need to install mpi4py through pip ...
Fig. 1. Publication year frequency chart (1984–2009). *Note: Some 2009 papers were not available in the databases at the time of paper collection, resulting in the drop in paper numbers in 2009. The number of publications appeared to be quite small during the period from 1984 to 1996. ...
对于中文A语言与文学的新指南(2021年首次评估),由于减少了Internal Assessment的评估项目,Paper1与Paper2的分值增加,从之前2015年首次评估的旧指南占的分值25%增加到35%,换句话说,拿下Paper1和Paper2就是拿下70%的江山!因此,了解新指南的要求,是攻城略地的第一步!今天我们就从新的Paper1讲起。
As stated in the original paper37,“In the informed consent procedure, [the subjects] explicitly consented for the anonymized collected data to be used for research purposes by other researchers. [..] The study was approved by the local ethics committee (CMO—the local “Committee on Research ...
“embeddings”) generated by these circuits. In this paper, we instead analyze the circuit computations directly: we deconstruct these computations into the functionally-specialized “transformations” that integrate contextual information across words. Using functional MRI data acquired while participants ...