89 research outputs found
A Novel Task of Loading and Computing Resource Scheduling Strategy in Internet of Vehicles Based on Dynamic Greedy Algorithm
Focus on the scheduling problem of distributed computing tasks in Internet of Vehicles. Firstly, based on the computing-aware network theory, a distributed computing resource model of the Internet of Vehicles is established, and the seven-dimensional QoS attributes of the computing resources in the Internet of Vehicles (reliability between computing resources, communication costs, computing speed and computing costs of the computing resources themselves , computing energy consumption, computing stability, and computing success rate) are grouped and transformed into two-dimensional comprehensive attribute priorities: computing performance priority and communication performance priority. Secondly, the weighted directed acyclic graph model of distributed computing tasks in the Internet of Vehicles and the seven-dimensional QoS attribute weighted undirected topology graph model of distributed computing resources in the Internet of Vehicles are respectively established. Moreover, a dynamic greedy algorithm-based task of loading and computing resource scheduling algorithm is proposed. Finally, the example analysis shows that the overall performance of this dynamic greedy algorithm-based task of loading and computing resource scheduling algorithm is better than the classic HEFT scheduling algorithm and round robin scheduling algorithm
GPT-NER: Named Entity Recognition via Large Language Models
Despite the fact that large-scale Language Models (LLM) have achieved SOTA
performances on a variety of NLP tasks, its performance on NER is still
significantly below supervised baselines. This is due to the gap between the
two tasks the NER and LLMs: the former is a sequence labeling task in nature
while the latter is a text-generation model.
In this paper, we propose GPT-NER to resolve this issue. GPT-NER bridges the
gap by transforming the sequence labeling task to a generation task that can be
easily adapted by LLMs e.g., the task of finding location entities in the input
text "Columbus is a city" is transformed to generate the text sequence
"@@Columbus## is a city", where special tokens @@## marks the entity to
extract. To efficiently address the "hallucination" issue of LLMs, where LLMs
have a strong inclination to over-confidently label NULL inputs as entities, we
propose a self-verification strategy by prompting LLMs to ask itself whether
the extracted entities belong to a labeled entity tag.
We conduct experiments on five widely adopted NER datasets, and GPT-NER
achieves comparable performances to fully supervised baselines, which is the
first time as far as we are concerned. More importantly, we find that GPT-NER
exhibits a greater ability in the low-resource and few-shot setups, when the
amount of training data is extremely scarce, GPT-NER performs significantly
better than supervised models. This demonstrates the capabilities of GPT-NER in
real-world NER applications where the number of labeled examples is limited
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN
To better handle long-tail cases in the sequence labeling (SL) task, in this
work, we introduce graph neural networks sequence labeling (GNN-SL), which
augments the vanilla SL model output with similar tagging examples retrieved
from the whole training set. Since not all the retrieved tagging examples
benefit the model prediction, we construct a heterogeneous graph, and leverage
graph neural networks (GNNs) to transfer information between the retrieved
tagging examples and the input word sequence. The augmented node which
aggregates information from neighbors is used to do prediction. This strategy
enables the model to directly acquire similar tagging examples and improves the
general quality of predictions. We conduct a variety of experiments on three
typical sequence labeling tasks: Named Entity Recognition (NER), Part of Speech
Tagging (POS), and Chinese Word Segmentation (CWS) to show the significant
performance of our GNN-SL. Notably, GNN-SL achieves SOTA results of 96.9 (+0.2)
on PKU, 98.3 (+0.4) on CITYU, 98.5 (+0.2) on MSR, and 96.9 (+0.2) on AS for the
CWS task, and results comparable to SOTA performances on NER datasets, and POS
datasets.Comment: preprin
Pushing the Limits of ChatGPT on NLP Tasks
Despite the success of ChatGPT, its performances on most NLP tasks are still
well below the supervised baselines. In this work, we looked into the causes,
and discovered that its subpar performance was caused by the following factors:
(1) token limit in the prompt does not allow for the full utilization of the
supervised datasets; (2) mismatch between the generation nature of ChatGPT and
NLP tasks; (3) intrinsic pitfalls of LLMs models, e.g., hallucination, overly
focus on certain keywords, etc.
In this work, we propose a collection of general modules to address these
issues, in an attempt to push the limits of ChatGPT on NLP tasks. Our proposed
modules include (1) a one-input-multiple-prompts strategy that employs multiple
prompts for one input to accommodate more demonstrations; (2) using fine-tuned
models for better demonstration retrieval; (3) transforming tasks to formats
that are more tailored to the generation nature; (4) employing reasoning
strategies that are tailored to addressing the task-specific complexity; (5)
the self-verification strategy to address the hallucination issue of LLMs; (6)
the paraphrase strategy to improve the robustness of model predictions.
We conduct experiments on 21 datasets of 10 representative NLP tasks,
including question answering, commonsense reasoning, natural language
inference, sentiment analysis, named entity recognition, entity-relation
extraction, event extraction, dependency parsing, semantic role labeling, and
part-of-speech tagging. Using the proposed assemble of techniques, we are able
to significantly boost the performance of ChatGPT on the selected NLP tasks,
achieving performances comparable to or better than supervised baselines, or
even existing SOTA performances
Quantum-enhanced Electrometer based on Microwave-dressed Rydberg Atoms
Rydberg atoms have been shown remarkable performance in sensing microwave
field. The sensitivity of such an electrometer based on optical readout of
atomic ensemble has been demonstrated to approach the photon-shot-noise limit.
However, the sensitivity can not be promoted infinitely by increasing the power
of probe light due to the increased collision rates and power broadening.
Compared with classical light, the use of quantum light may lead to a better
sensitivity with lower number of photons. In this paper, we exploit
entanglement in a microwave-dressed Rydberg electrometer to suppress the
fluctuation of noise. The results show a sensitivity enhancement beating the
shot noise limit in both cold and hot atom schemes. Through optimizing the
transmission of optical readout, our quantum advantage can be maintained with
different absorptive index of atomic vapor, which makes it possible to apply
quantum light source in the absorptive electrometer
Instruction Tuning for Large Language Models: A Survey
This paper surveys research works in the quickly advancing field of
instruction tuning (IT), a crucial technique to enhance the capabilities and
controllability of large language models (LLMs). Instruction tuning refers to
the process of further training LLMs on a dataset consisting of
\textsc{(instruction, output)} pairs in a supervised fashion, which bridges the
gap between the next-word prediction objective of LLMs and the users' objective
of having LLMs adhere to human instructions. In this work, we make a systematic
review of the literature, including the general methodology of IT, the
construction of IT datasets, the training of IT models, and applications to
different modalities, domains and applications, along with an analysis on
aspects that influence the outcome of IT (e.g., generation of instruction
outputs, size of the instruction dataset, etc). We also review the potential
pitfalls of IT along with criticism against it, along with efforts pointing out
current deficiencies of existing strategies and suggest some avenues for
fruitful research.Comment: A Survey paper, Pre-prin
Annealing novel nucleobase-lipids with oligonucleotides or plasmid DNA based on H-bonding or π-π interaction:Assemblies and transfections
Lipid derivatives of nucleoside analogs have been highlighted for their potential for effective gene delivery. A novel class of nucleobase-lipids are rationally designed and readily synthesized, comprising thymine/cytosine, an ester/amide linker and an oleyl lipid. The diversity of four nucleobase-lipids termed DXBAs (DOTA, DNTA, DOCA and DNCA) is investigated. Besides, DNCA is demonstrated to be an effective neutral transfection material for nucleic acid delivery, which enbles to bind to oligonucleotides via H-bonding and π-π stacking with reduced toxicity in vitro and in vivo. Several kinds of nucleic acid drugs including aptamer, ssRNA, antisense oligonucleotide, and plasmid DNAs can be delivered by DXBAs, especially DNCA. In particular, G4-aptamer AS1411 encapsulated by DNCA exhibits cellular uptake enhancement, lysosome degradation reduction, cell apoptosis promotion, cell cycle phase alteration in vitro and duration prolongation in vivo, resulting in significant anti-proliferative activity. Our results demonstrate that DNCA is a promising transfection agent for G4-aptamers and exhibites bright application prospects in the permeation improvement of single-stranded oligonucleotides or plasmid DNAs
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