129 research outputs found
Experimental study of mechanical property for prestressed rubber bearing
To overcome the shortages of existing Rubber Bearings (RBs), an innovative type of isolator, named as Prestressed Rubber Bearing (PRB), is presented in this paper. Base on conventional laminated Rubber Bearing (RB), PRB is developed by increasing the thickness of rubber layers, setting vertical ducts and installing prestress tendons. Through the vertical and horizontal monotonic loading test, the vertical and horizontal stiffness of PRBs are investigated. The empirical formulas for stiffness are proposed. Moreover, the hysteresis behavior and the energy dissipation capacity of PRBs are studied by reversed cyclic loading test. The results show that PRBs not only have the horizontal isolating capacity as conventional RBs, but also have the capacity of horizontal displacement-limitation and improved capacity of energy dissipation
AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities
Multi-modal knowledge graphs (MMKGs) combine different modal data (e.g., text
and image) for a comprehensive understanding of entities. Despite the recent
progress of large-scale MMKGs, existing MMKGs neglect the multi-aspect nature
of entities, limiting the ability to comprehend entities from various
perspectives. In this paper, we construct AspectMMKG, the first MMKG with
aspect-related images by matching images to different entity aspects.
Specifically, we collect aspect-related images from a knowledge base, and
further extract aspect-related sentences from the knowledge base as queries to
retrieve a large number of aspect-related images via an online image search
engine. Finally, AspectMMKG contains 2,380 entities, 18,139 entity aspects, and
645,383 aspect-related images. We demonstrate the usability of AspectMMKG in
entity aspect linking (EAL) downstream task and show that previous EAL models
achieve a new state-of-the-art performance with the help of AspectMMKG. To
facilitate the research on aspect-related MMKG, we further propose an
aspect-related image retrieval (AIR) model, that aims to correct and expand
aspect-related images in AspectMMKG. We train an AIR model to learn the
relationship between entity image and entity aspect-related images by
incorporating entity image, aspect, and aspect image information. Experimental
results indicate that the AIR model could retrieve suitable images for a given
entity w.r.t different aspects.Comment: Accepted by CIKM 202
Zero-Shot Cross-Lingual Summarization via Large Language Models
Given a document in a source language, cross-lingual summarization (CLS) aims
to generate a summary in a different target language. Recently, the emergence
of Large Language Models (LLMs), such as GPT-3.5, ChatGPT and GPT-4, has
attracted wide attention from the computational linguistics community. However,
it is not yet known the performance of LLMs on CLS. In this report, we
empirically use various prompts to guide LLMs to perform zero-shot CLS from
different paradigms (i.e., end-to-end and pipeline), and provide a preliminary
evaluation on the generated summaries. We find that ChatGPT and GPT-4
originally prefer to produce lengthy summaries with detailed information. These
two LLMs can further balance informativeness and conciseness with the help of
an interactive prompt, significantly improving their CLS performance.
Experimental results on three widely-used CLS datasets show that GPT-4 achieves
state-of-the-art zero-shot CLS performance, and performs competitively compared
with the fine-tuned mBART-50. Moreover, we also find some multi-lingual and
bilingual LLMs (i.e., BLOOMZ, ChatGLM-6B, Vicuna-13B and ChatYuan) have limited
zero-shot CLS ability. Due to the composite nature of CLS, which requires
models to perform summarization and translation simultaneously, accomplishing
this task in a zero-shot manner is even a challenge for LLMs. Therefore, we
sincerely hope and recommend future LLM research could use CLS as a testbed.Comment: Technical Report, 11 page
Rethinking Normalization Methods in Federated Learning
Federated learning (FL) is a popular distributed learning framework that can
reduce privacy risks by not explicitly sharing private data. In this work, we
explicitly uncover external covariate shift problem in FL, which is caused by
the independent local training processes on different devices. We demonstrate
that external covariate shifts will lead to the obliteration of some devices'
contributions to the global model. Further, we show that normalization layers
are indispensable in FL since their inherited properties can alleviate the
problem of obliterating some devices' contributions. However, recent works have
shown that batch normalization, which is one of the standard components in many
deep neural networks, will incur accuracy drop of the global model in FL. The
essential reason for the failure of batch normalization in FL is poorly
studied. We unveil that external covariate shift is the key reason why batch
normalization is ineffective in FL. We also show that layer normalization is a
better choice in FL which can mitigate the external covariate shift and improve
the performance of the global model. We conduct experiments on CIFAR10 under
non-IID settings. The results demonstrate that models with layer normalization
converge fastest and achieve the best or comparable accuracy for three
different model architectures.Comment: Submitted to DistributedML'22 worksho
Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model
Constructing commonsense knowledge graphs (CKGs) has attracted wide research
attention due to its significant importance in cognitive intelligence.
Nevertheless, existing CKGs are typically oriented to English, limiting the
research in non-English languages. Meanwhile, the emergence of foundation
models like ChatGPT and GPT-4 has shown promising intelligence with the help of
reinforcement learning from human feedback. Under the background, in this
paper, we utilize foundation models to construct a Chinese CKG, named Snowman.
Specifically, we distill different types of commonsense head items from
ChatGPT, and continue to use it to collect tail items with respect to the head
items and pre-defined relations. Based on the preliminary analysis, we find the
negative commonsense knowledge distilled by ChatGPT achieves lower human
acceptance compared to other knowledge. Therefore, we design a simple yet
effective self-instruct filtering strategy to filter out invalid negative
commonsense. Overall, the constructed Snowman covers more than ten million
Chinese commonsense triples, making it the largest Chinese CKG. Moreover, human
studies show the acceptance of Snowman achieves 90.6\%, indicating the
high-quality triples distilled by the cutting-edge foundation model. We also
conduct experiments on commonsense knowledge models to show the usability and
effectiveness of our Snowman.Comment: tech repor
Understanding Translationese in Cross-Lingual Summarization
Given a document in a source language, cross-lingual summarization (CLS) aims
at generating a concise summary in a different target language. Unlike
monolingual summarization (MS), naturally occurring source-language documents
paired with target-language summaries are rare. To collect large-scale CLS
data, existing datasets typically involve translation in their creation.
However, the translated text is distinguished from the text originally written
in that language, i.e., translationese. In this paper, we first confirm that
different approaches of constructing CLS datasets will lead to different
degrees of translationese. Then we systematically investigate how
translationese affects CLS model evaluation and performance when it appears in
source documents or target summaries. In detail, we find that (1) the
translationese in documents or summaries of test sets might lead to the
discrepancy between human judgment and automatic evaluation; (2) the
translationese in training sets would harm model performance in real-world
applications; (3) though machine-translated documents involve translationese,
they are very useful for building CLS systems on low-resource languages under
specific training strategies. Lastly, we give suggestions for future CLS
research including dataset and model developments. We hope that our work could
let researchers notice the phenomenon of translationese in CLS and take it into
account in the future.Comment: Accepted to the Findings of EMNLP 202
SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models
Mixture-of-Experts (MoE) has emerged as a favorable architecture in the era
of large models due to its inherent advantage, i.e., enlarging model capacity
without incurring notable computational overhead. Yet, the realization of such
benefits often results in ineffective GPU memory utilization, as large portions
of the model parameters remain dormant during inference. Moreover, the memory
demands of large models consistently outpace the memory capacity of
contemporary GPUs. Addressing this, we introduce SiDA (Sparsity-inspired
Data-Aware), an efficient inference approach tailored for large MoE models.
SiDA judiciously exploits both the system's main memory, which is now abundant
and readily scalable, and GPU memory by capitalizing on the inherent sparsity
on expert activation in MoE models. By adopting a data-aware perspective, SiDA
achieves enhanced model efficiency with a neglectable performance drop.
Specifically, SiDA attains a remarkable speedup in MoE inference with up to
3.93X throughput increasing, up to 75% latency reduction, and up to 80% GPU
memory saving with down to 1% performance drop. This work paves the way for
scalable and efficient deployment of large MoE models, even in
memory-constrained systems
Is ChatGPT a Good NLG Evaluator? A Preliminary Study
Recently, the emergence of ChatGPT has attracted wide attention from the
computational linguistics community. Many prior studies have shown that ChatGPT
achieves remarkable performance on various NLP tasks in terms of automatic
evaluation metrics. However, the ability of ChatGPT to serve as an evaluation
metric is still underexplored. Considering assessing the quality of natural
language generation (NLG) models is an arduous task and NLG metrics notoriously
show their poor correlation with human judgments, we wonder whether ChatGPT is
a good NLG evaluation metric. In this report, we provide a preliminary
meta-evaluation on ChatGPT to show its reliability as an NLG metric. In detail,
we regard ChatGPT as a human evaluator and give task-specific (e.g.,
summarization) and aspect-specific (e.g., relevance) instruction to prompt
ChatGPT to evaluate the generated results of NLG models. We conduct experiments
on five NLG meta-evaluation datasets (including summarization, story generation
and data-to-text tasks). Experimental results show that compared with previous
automatic metrics, ChatGPT achieves state-of-the-art or competitive correlation
with human judgments in most cases. In addition, we find that the effectiveness
of the ChatGPT evaluator might be influenced by the creation method of the
meta-evaluation datasets. For the meta-evaluation datasets which are created
greatly depending on the reference and thus are biased, the ChatGPT evaluator
might lose its effectiveness. We hope our preliminary study could prompt the
emergence of a general-purposed reliable NLG metric.Comment: Both first authors contributed equally. Technical Report, 11 pages.
Accepted to the 4th New Frontiers in Summarization Workshop (NewSumm@EMNLP
2023
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