131 research outputs found
Leakage Analysis and Solution of the RFID Analog Front-END
The identification and modeling of different leakage components are very important for estimation and reduction of leakage power, especially low-power applications, such as RFID chip. This paper proposes a theory about leakage mechanism of RFID chip and proves the theory. The one contribution of the paper is the proposed theory about leakage mechanism of RFID chip. The other contribution is that it proves the differences between tape-out verification results and computer simulation results and that to what degree the differences occur for different circuits. And when the source potential is much lower than the substrate potential, tape-out verification results and computer simulation results have larger differences. The test results show that the actual leakage power increases 26.3 times compares with the computer simulation results’ when the source potential is -750mV
Evolving Knowledge Distillation with Large Language Models and Active Learning
Large language models (LLMs) have demonstrated remarkable capabilities across
various NLP tasks. However, their computational costs are prohibitively high.
To address this issue, previous research has attempted to distill the knowledge
of LLMs into smaller models by generating annotated data. Nonetheless, these
works have mainly focused on the direct use of LLMs for text generation and
labeling, without fully exploring their potential to comprehend the target task
and acquire valuable knowledge. In this paper, we propose EvoKD: Evolving
Knowledge Distillation, which leverages the concept of active learning to
interactively enhance the process of data generation using large language
models, simultaneously improving the task capabilities of small domain model
(student model). Different from previous work, we actively analyze the student
model's weaknesses, and then synthesize labeled samples based on the analysis.
In addition, we provide iterative feedback to the LLMs regarding the student
model's performance to continuously construct diversified and challenging
samples. Experiments and analysis on different NLP tasks, namely, text
classification and named entity recognition show the effectiveness of EvoKD.Comment: Accepted by COLING 202
Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery
While self-supervised graph pretraining techniques have shown promising
results in various domains, their application still experiences challenges of
limited topology learning, human knowledge dependency, and incompetent
multi-level interactions. To address these issues, we propose a novel solution,
Dual-level Graph self-supervised Pretraining with Motif discovery (DGPM), which
introduces a unique dual-level pretraining structure that orchestrates
node-level and subgraph-level pretext tasks. Unlike prior approaches, DGPM
autonomously uncovers significant graph motifs through an edge pooling module,
aligning learned motif similarities with graph kernel-based similarities. A
cross-matching task enables sophisticated node-motif interactions and novel
representation learning. Extensive experiments on 15 datasets validate DGPM's
effectiveness and generalizability, outperforming state-of-the-art methods in
unsupervised representation learning and transfer learning settings. The
autonomously discovered motifs demonstrate the potential of DGPM to enhance
robustness and interpretability.Comment: 14 pages, 6 figures, accepted by AAAI'2
Goal-Oriented Prompt Attack and Safety Evaluation for LLMs
Large Language Models (LLMs) presents significant priority in text
understanding and generation. However, LLMs suffer from the risk of generating
harmful contents especially while being employed to applications. There are
several black-box attack methods, such as Prompt Attack, which can change the
behaviour of LLMs and induce LLMs to generate unexpected answers with harmful
contents. Researchers are interested in Prompt Attack and Defense with LLMs,
while there is no publicly available dataset with high successful attacking
rate to evaluate the abilities of defending prompt attack. In this paper, we
introduce a pipeline to construct high-quality prompt attack samples, along
with a Chinese prompt attack dataset called CPAD. Our prompts aim to induce
LLMs to generate unexpected outputs with several carefully designed prompt
attack templates and widely concerned attacking contents. Different from
previous datasets involving safety estimation, we construct the prompts
considering three dimensions: contents, attacking methods and goals.
Especially, the attacking goals indicate the behaviour expected after
successfully attacking the LLMs, thus the responses can be easily evaluated and
analysed. We run several popular Chinese LLMs on our dataset, and the results
show that our prompts are significantly harmful to LLMs, with around 70% attack
success rate to GPT-3.5. CPAD is publicly available at
https://github.com/liuchengyuan123/CPAD
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