898 research outputs found
Application of Fireproof Coating for New Energy Vehicle Battery Pack
In the development process of new energy vehicles, the battery pack is one of the key parts, and the safety of the battery pack has always been an important factor affecting the application range and market sales of new energy vehicles. In order to improve the safety of battery packs, fireproof coatings are widely used on the surface of battery packs. This paper introduces the application of fireproof coatings in new energy vehicles by analyzing the composition and function of fireproof coatings
Variation of POD Activity in Roots of Different Fall Dormancy Alfalfa under the Autumn Cutting
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages
Chain-of-thought (CoT) is capable of eliciting models to explicitly generate
reasoning paths, thus promoting reasoning accuracy and attracting increasing
attention. Specifically, zero-shot CoT achieves remarkable improvements in a
wide range of reasoning tasks by simply instructing the LLM with the prompt
"Let's think step by step!". Despite the success of zero-shot CoT, the existing
zero-shot prompting techniques remain limited to a single language, making it
challenging to generalize to other languages and hindering global development.
In this work, we introduce cross-lingual prompting (CLP), aiming to improve
zero-shot CoT reasoning across languages. Specifically, CLP consists of two
main components: (1) cross-lingual alignment prompting and (2) task-specific
solver prompting. The cross-lingual alignment prompting is responsible for
aligning representations across different languages, whereas the task-specific
solver prompting is used to generate the final chain of thoughts and results
for the reasoning task. In addition, we further introduce cross-lingual
self-consistent prompting (CLSP) to ensemble different reasoning paths across
languages. Our experimental evaluations on several benchmarks demonstrate that
CLP and CLSP significantly outperform the existing prompting methods and
achieve state-of-the-art performance. We hope this work will inspire further
breakthroughs in cross-lingual CoT.Comment: Accepted at EMNLP2023 Main Conferenc
A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding
Zero-shot dialogue understanding aims to enable dialogue to track the user's
needs without any training data, which has gained increasing attention. In this
work, we investigate the understanding ability of ChatGPT for zero-shot
dialogue understanding tasks including spoken language understanding (SLU) and
dialogue state tracking (DST). Experimental results on four popular benchmarks
reveal the great potential of ChatGPT for zero-shot dialogue understanding. In
addition, extensive analysis shows that ChatGPT benefits from the multi-turn
interactive prompt in the DST task but struggles to perform slot filling for
SLU. Finally, we summarize several unexpected behaviors of ChatGPT in dialogue
understanding tasks, hoping to provide some insights for future research on
building zero-shot dialogue understanding systems with Large Language Models
(LLMs).Comment: Technical Repor
Mechanical Properties of Recycled Aggregate Concrete Modified by Nano-particles
In this study, different nano-particles were used to modify recycled aggregates concrete (RAC) containing recycled clay brick aggregates (RCBAs) to improve the RAC properties. Two stages of experimental works were performed. In the first stage, various nano-particle mixtures produced by different mixing methods, i.e. the use of surfactant and ultrasonication, were examined by optical microscope to evaluate the dispersion of the nano-particles in water liquid. The nano-particles modified cement mortar specimens were further evaluated by flexural tensile test to check how these mixing methods affect the properties of the nano-particle modified cement mortar. In the second experimental stage, the effects of four replacement ratios of recycled aggregates, three type of nano-particles, two mixing methods of RAC, additional surfactant and ultrasonication process used in the mix of nano-particle liquid, and the dosages of the nano-particles on the workability, compressive and split tensile properties of the nano-particle modified RAC were investigated
Text with Knowledge Graph Augmented Transformer for Video Captioning
Video captioning aims to describe the content of videos using natural
language. Although significant progress has been made, there is still much room
to improve the performance for real-world applications, mainly due to the
long-tail words challenge. In this paper, we propose a text with knowledge
graph augmented transformer (TextKG) for video captioning. Notably, TextKG is a
two-stream transformer, formed by the external stream and internal stream. The
external stream is designed to absorb additional knowledge, which models the
interactions between the additional knowledge, e.g., pre-built knowledge graph,
and the built-in information of videos, e.g., the salient object regions,
speech transcripts, and video captions, to mitigate the long-tail words
challenge. Meanwhile, the internal stream is designed to exploit the
multi-modality information in videos (e.g., the appearance of video frames,
speech transcripts, and video captions) to ensure the quality of caption
results. In addition, the cross attention mechanism is also used in between the
two streams for sharing information. In this way, the two streams can help each
other for more accurate results. Extensive experiments conducted on four
challenging video captioning datasets, i.e., YouCookII, ActivityNet Captions,
MSRVTT, and MSVD, demonstrate that the proposed method performs favorably
against the state-of-the-art methods. Specifically, the proposed TextKG method
outperforms the best published results by improving 18.7% absolute CIDEr scores
on the YouCookII dataset.Comment: Accepted by CVPR202
- …