15,078 research outputs found
A concise review of recent few-shot meta-learning methods
Few-shot meta-learning has been recently reviving with expectations to mimic humanity’s fast adaption to new concepts based on prior knowledge. In this short communication, we give a concise review on recent representative methods in few-shot meta-learning, which are categorized into four branches according to their technical characteristics. We conclude this review with some vital current challenges and future prospects in few-shot meta-learning
Leveraging GPT-4 for Food Effect Summarization to Enhance Product-Specific Guidance Development via Iterative Prompting
Food effect summarization from New Drug Application (NDA) is an essential
component of product-specific guidance (PSG) development and assessment.
However, manual summarization of food effect from extensive drug application
review documents is time-consuming, which arouses a need to develop automated
methods. Recent advances in large language models (LLMs) such as ChatGPT and
GPT-4, have demonstrated great potential in improving the effectiveness of
automated text summarization, but its ability regarding the accuracy in
summarizing food effect for PSG assessment remains unclear. In this study, we
introduce a simple yet effective approach, iterative prompting, which allows
one to interact with ChatGPT or GPT-4 more effectively and efficiently through
multi-turn interaction. Specifically, we propose a three-turn iterative
prompting approach to food effect summarization in which the keyword-focused
and length-controlled prompts are respectively provided in consecutive turns to
refine the quality of the generated summary. We conduct a series of extensive
evaluations, ranging from automated metrics to FDA professionals and even
evaluation by GPT-4, on 100 NDA review documents selected over the past five
years. We observe that the summary quality is progressively improved throughout
the process. Moreover, we find that GPT-4 performs better than ChatGPT, as
evaluated by FDA professionals (43% vs. 12%) and GPT-4 (64% vs. 35%).
Importantly, all the FDA professionals unanimously rated that 85% of the
summaries generated by GPT-4 are factually consistent with the golden reference
summary, a finding further supported by GPT-4 rating of 72% consistency. These
results strongly suggest a great potential for GPT-4 to draft food effect
summaries that could be reviewed by FDA professionals, thereby improving the
efficiency of PSG assessment cycle and promoting the generic drug product
development.Comment: 22 pages, 6 figure
Speed/accuracy trade-offs for modern convolutional object detectors
The goal of this paper is to serve as a guide for selecting a detection
architecture that achieves the right speed/memory/accuracy balance for a given
application and platform. To this end, we investigate various ways to trade
accuracy for speed and memory usage in modern convolutional object detection
systems. A number of successful systems have been proposed in recent years, but
apples-to-apples comparisons are difficult due to different base feature
extractors (e.g., VGG, Residual Networks), different default image resolutions,
as well as different hardware and software platforms. We present a unified
implementation of the Faster R-CNN [Ren et al., 2015], R-FCN [Dai et al., 2016]
and SSD [Liu et al., 2015] systems, which we view as "meta-architectures" and
trace out the speed/accuracy trade-off curve created by using alternative
feature extractors and varying other critical parameters such as image size
within each of these meta-architectures. On one extreme end of this spectrum
where speed and memory are critical, we present a detector that achieves real
time speeds and can be deployed on a mobile device. On the opposite end in
which accuracy is critical, we present a detector that achieves
state-of-the-art performance measured on the COCO detection task.Comment: Accepted to CVPR 201
ChatAgri: Exploring Potentials of ChatGPT on Cross-linguistic Agricultural Text Classification
In the era of sustainable smart agriculture, a massive amount of agricultural
news text is being posted on the Internet, in which massive agricultural
knowledge has been accumulated. In this context, it is urgent to explore
effective text classification techniques for users to access the required
agricultural knowledge with high efficiency. Mainstream deep learning
approaches employing fine-tuning strategies on pre-trained language models
(PLMs), have demonstrated remarkable performance gains over the past few years.
Nonetheless, these methods still face many drawbacks that are complex to solve,
including: 1. Limited agricultural training data due to the expensive-cost and
labour-intensive annotation; 2. Poor domain transferability, especially of
cross-linguistic ability; 3. Complex and expensive large models
deployment.Inspired by the extraordinary success brought by the recent ChatGPT
(e.g. GPT-3.5, GPT-4), in this work, we systematically investigate and explore
the capability and utilization of ChatGPT applying to the agricultural
informatization field. ....(shown in article).... Code has been released on
Github
https://github.com/albert-jin/agricultural_textual_classification_ChatGPT.Comment: 24 pages,10+figures,46references.Both the first two authors, Biao
Zhao and Weiqiang Jin, made equal contributions to this work. Corresponding
author: Guang Yan
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