Dr. LLaMA: Improving Small Language Models in Domain-Specific QA via Generative Data Augmentation
This paper introduces Dr. LLaMA, a method for improving SLMs through generative data augmentation using LLMs, focusing on medical question-answering tasks and the PubMedQA dataset. The findings indicate that LLMs effectively refine and diversify existing question-answer pairs, resulting in improved performance of a much smaller model on domain-specific QA datasets after fine-tuning. This study suggests potential research directions to create more efficient and capable models for specialized applications.
Dr. LLaMA can be used to improve small language models in domain-specific question-answering tasks. By leveraging larger language models, Dr. LLaMA can refine and diversify existing question-answer pairs to improve performance. This can be valuable for businesses in the healthcare industry or other specialized fields that require domain-specific language models.
AutoRecon: Automated 3D Object Discovery and Reconstruction
This paper proposes a novel framework named AutoRecon for the automated discovery and reconstruction of an object from multi-view images. AutoRecon can robustly locate and segment foreground objects from SfM point clouds by leveraging self-supervised 2D vision transformer features, then reconstruct decomposed neural scene representations with dense supervision provided by the decomposed point clouds, resulting in accurate object reconstruction and segmentation. Experiments on the DTU, BlendedMVS and CO3D-V2 datasets demonstrate the effectiveness and robustness of AutoRecon.
AutoRecon can be used for automated object reconstruction and segmentation, which can save businesses time and resources on manual labeling and annotation tasks. This can be valuable for digital content creation companies or businesses that utilize 3D object models.
DarkBERT: A Language Model for the Dark Side of the Internet
This paper introduces DarkBERT, a language model pretrained on Dark Web data. DarkBERT was trained to combat the extreme lexical and structural diversity of the Dark Web that may be detrimental to building a proper representation of the domain. The evaluations show that DarkBERT outperforms current language models and may serve as a valuable resource for future research on the Dark Web.
DarkBERT can be used to analyze the language used in the Dark Web, providing valuable insights to researchers. This can be valuable for businesses that need to monitor and analyze the Dark Web for security or other reasons.
CodeT5+: Open Code Large Language Models for Code Understanding and Generation
CodeT5+ is a family of encoder-decoder LLMs for code that can be flexibly combined to suit a wide range of downstream code tasks. They propose a mixture of pretraining objectives to mitigate the pretrain-finetune discrepancy and explore instruction-tuning to align with natural language instructions. They observe state-of-the-art model performance on various code-related tasks.
Implement CodeT5+ to improve code-related tasks and achieve state-of-the-art performance. Consider using a mixture of pretraining objectives to mitigate the pretrain-finetune discrepancy and instruction-tuning to align with natural language instructions.
Small Models are Valuable Plug-ins for Large Language Models
Super In-Context Learning (SuperICL) allows black-box LLMs to work with locally fine-tuned smaller models, resulting in superior performance on supervised tasks. SuperICL improves performance beyond state-of-the-art fine-tuned models while addressing the instability problem of in-context learning. Furthermore, SuperICL can enhance the capabilities of smaller models, such as multilinguality and interpretability.
Consider using Super In-Context Learning (SuperICL) to improve performance on supervised tasks beyond traditional fine-tuning methods. SuperICL can also enhance the capabilities of smaller models, such as multilinguality and interpretability.