251 research outputs found
Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model Control
Pretrained language models have demonstrated extraordinary capabilities in
language generation. However, real-world tasks often require controlling the
distribution of generated text in order to mitigate bias, promote fairness, and
achieve personalization. Existing techniques for controlling the distribution
of generated text only work with quantified distributions, which require
pre-defined categories, proportions of the distribution, or an existing corpus
following the desired distributions. However, many important distributions,
such as personal preferences, are unquantified. In this work, we tackle the
problem of generating text following arbitrary distributions (quantified and
unquantified) by proposing Nano, a few-shot human-in-the-loop training
algorithm that continuously learns from human feedback. Nano achieves
state-of-the-art results on single topic/attribute as well as quantified
distribution control compared to previous works. We also show that Nano is able
to learn unquantified distributions, achieves personalization, and captures
differences between different individuals' personal preferences with high
sample efficiency.Comment: Accepted to ACL Findings 202
MultiZoo & MultiBench: A Standardized Toolkit for Multimodal Deep Learning
Learning multimodal representations involves integrating information from
multiple heterogeneous sources of data. In order to accelerate progress towards
understudied modalities and tasks while ensuring real-world robustness, we
release MultiZoo, a public toolkit consisting of standardized implementations
of > 20 core multimodal algorithms and MultiBench, a large-scale benchmark
spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas.
Together, these provide an automated end-to-end machine learning pipeline that
simplifies and standardizes data loading, experimental setup, and model
evaluation. To enable holistic evaluation, we offer a comprehensive methodology
to assess (1) generalization, (2) time and space complexity, and (3) modality
robustness. MultiBench paves the way towards a better understanding of the
capabilities and limitations of multimodal models, while ensuring ease of use,
accessibility, and reproducibility. Our toolkits are publicly available, will
be regularly updated, and welcome inputs from the community.Comment: JMLR Open Source Software 2023, Code available at
https://github.com/pliang279/MultiBenc
SAMAug: Point Prompt Augmentation for Segment Anything Model
This paper introduces SAMAug, a novel visual point augmentation method for
the Segment Anything Model (SAM) that enhances interactive image segmentation
performance. SAMAug generates augmented point prompts to provide more
information about the user's intention to SAM. Starting with an initial point
prompt, SAM produces an initial mask, which is then fed into our proposed
SAMAug to generate augmented point prompts. By incorporating these extra
points, SAM can generate augmented segmentation masks based on both the
augmented point prompts and the initial prompt, resulting in improved
segmentation performance. We conducted evaluations using four different point
augmentation strategies: random sampling, sampling based on maximum difference
entropy, maximum distance, and saliency. Experiment results on the COCO,
Fundus, COVID QUEx, and ISIC2018 datasets show that SAMAug can boost SAM's
segmentation results, especially using the maximum distance and saliency.
SAMAug demonstrates the potential of visual prompt augmentation for computer
vision. Codes of SAMAug are available at github.com/yhydhx/SAMAu
Research progress of sophoridine’s pharmacological activities and its molecular mechanism: an updated review
Background: Sophoridine, the major active constituent of Sophora alopecuroides and its roots, is a bioactive alkaloid with a wide range of pharmacological effects, including antitumor, anti-inflammatory, antiviral, antibacterial, analgesic, cardioprotective, and immunoprotective activities. Sophora flavescens Aiton is a traditional Chinese medicine that is bitter and cold. Additionally, it also exhibits the effects of clearing heat, eliminating dampness, and expelling insects.Aims of the study: To summarize the pharmacological research and associated mechanisms of sophoridine, we compiled this review by combining a huge body of relevant literature.Materials and methods: The information related to this article was systematically collected from the scientific literature databases including PubMed, Google Scholar, Web of Science, Science Direct, Springer, China National Knowledge Infrastructure, published books, PhD and MS dissertations.Results: Its antitumor activity is particularly remarkable, as it can inhibit cancer cell proliferation, invasion, and metastasis while inducing cell cycle arrest and apoptosis. Additionally, sophoridine also holds therapeutic potential for myocardial ischemia, osteoporosis, arrhythmias, and neurological disorders, primarily through the suppression of related inflammatory factors and cell apoptosis. However, sophoridine has also exhibited adverse effects such as hepatotoxicity and neurotoxicity. The antidisease effect and mechanism of sophoridine are diverse, so it has high research value.Conclusion: As an important traditional Chinese medicine alkaloid, modern pharmacological studies have demonstrated that sophoridine has prominent bioactivities, especially on anti-tumor anti-inflammation activities, and cardiovascular system protection. These activities provide prospects for novel drug development for cancer and some chronic diseases. Nevertheless, the understanding of the multitarget network pharmacology, long-term in vivo toxicity, and clinical efficacy of sophoridine require further detailed research
RadOnc-GPT: A Large Language Model for Radiation Oncology
This paper presents RadOnc-GPT, a large language model specialized for
radiation oncology through advanced tuning methods. RadOnc-GPT was finetuned on
a large dataset of radiation oncology patient records and clinical notes from
the Mayo Clinic in Arizona. The model employs instruction tuning on three key
tasks - generating radiotherapy treatment regimens, determining optimal
radiation modalities, and providing diagnostic descriptions/ICD codes based on
patient diagnostic details. Evaluations conducted by comparing RadOnc-GPT
outputs to general large language model outputs showed that RadOnc-GPT
generated outputs with significantly improved clarity, specificity, and
clinical relevance. The study demonstrated the potential of using large
language models fine-tuned using domain-specific knowledge like RadOnc-GPT to
achieve transformational capabilities in highly specialized healthcare fields
such as radiation oncology
AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology
In this pioneering study, inspired by AutoGPT, the state-of-the-art
open-source application based on the GPT-4 large language model, we develop a
novel tool called AD-AutoGPT which can conduct data collection, processing, and
analysis about complex health narratives of Alzheimer's Disease in an
autonomous manner via users' textual prompts. We collated comprehensive data
from a variety of news sources, including the Alzheimer's Association, BBC,
Mayo Clinic, and the National Institute on Aging since June 2022, leading to
the autonomous execution of robust trend analyses, intertopic distance maps
visualization, and identification of salient terms pertinent to Alzheimer's
Disease. This approach has yielded not only a quantifiable metric of relevant
discourse but also valuable insights into public focus on Alzheimer's Disease.
This application of AD-AutoGPT in public health signifies the transformative
potential of AI in facilitating a data-rich understanding of complex health
narratives like Alzheimer's Disease in an autonomous manner, setting the
groundwork for future AI-driven investigations in global health landscapes.Comment: 20 pages, 4 figure
Artificial General Intelligence for Radiation Oncology
The emergence of artificial general intelligence (AGI) is transforming
radiation oncology. As prominent vanguards of AGI, large language models (LLMs)
such as GPT-4 and PaLM 2 can process extensive texts and large vision models
(LVMs) such as the Segment Anything Model (SAM) can process extensive imaging
data to enhance the efficiency and precision of radiation therapy. This paper
explores full-spectrum applications of AGI across radiation oncology including
initial consultation, simulation, treatment planning, treatment delivery,
treatment verification, and patient follow-up. The fusion of vision data with
LLMs also creates powerful multimodal models that elucidate nuanced clinical
patterns. Together, AGI promises to catalyze a shift towards data-driven,
personalized radiation therapy. However, these models should complement human
expertise and care. This paper provides an overview of how AGI can transform
radiation oncology to elevate the standard of patient care in radiation
oncology, with the key insight being AGI's ability to exploit multimodal
clinical data at scale
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