71 research outputs found
On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions
As Federated Learning (FL) has gained increasing attention, it has become
widely acknowledged that straightforwardly applying stochastic gradient descent
(SGD) on the overall framework when learning over a sequence of tasks results
in the phenomenon known as ``catastrophic forgetting''. Consequently, much FL
research has centered on devising federated increasing learning methods to
alleviate forgetting while augmenting knowledge. On the other hand, forgetting
is not always detrimental. The selective amnesia, also known as federated
unlearning, which entails the elimination of specific knowledge, can address
privacy concerns and create additional ``space'' for acquiring new knowledge.
However, there is a scarcity of extensive surveys that encompass recent
advancements and provide a thorough examination of this issue. In this
manuscript, we present an extensive survey on the topic of knowledge editing
(augmentation/removal) in Federated Learning, with the goal of summarizing the
state-of-the-art research and expanding the perspective for various domains.
Initially, we introduce an integrated paradigm, referred to as Federated
Editable Learning (FEL), by reevaluating the entire lifecycle of FL. Secondly,
we provide a comprehensive overview of existing methods, evaluate their
position within the proposed paradigm, and emphasize the current challenges
they face. Lastly, we explore potential avenues for future research and
identify unresolved issues.Comment: 7 pages, 1 figure, 2 tabel
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL
task. However, the absence of a systematical benchmark inhibits the development
of designing effective, efficient and economic LLM-based Text-to-SQL solutions.
To address this challenge, in this paper, we first conduct a systematical and
extensive comparison over existing prompt engineering methods, including
question representation, example selection and example organization, and with
these experimental results, we elaborate their pros and cons. Based on these
findings, we propose a new integrated solution, named DAIL-SQL, which refreshes
the Spider leaderboard with 86.6% execution accuracy and sets a new bar. To
explore the potential of open-source LLM, we investigate them in various
scenarios, and further enhance their performance with supervised fine-tuning.
Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well
as the advantages and disadvantages of the supervised fine-tuning.
Additionally, towards an efficient and economic LLM-based Text-to-SQL solution,
we emphasize the token efficiency in prompt engineering and compare the prior
studies under this metric. We hope that our work provides a deeper
understanding of Text-to-SQL with LLMs, and inspires further investigations and
broad applications.Comment: We have released code on https://github.com/BeachWang/DAIL-SQ
Unicron: Economizing Self-Healing LLM Training at Scale
Training large-scale language models is increasingly critical in various
domains, but it is hindered by frequent failures, leading to significant time
and economic costs. Current failure recovery methods in cloud-based settings
inadequately address the diverse and complex scenarios that arise, focusing
narrowly on erasing downtime for individual tasks without considering the
overall cost impact on a cluster. We introduce Unicron, a workload manager
designed for efficient self-healing in large-scale language model training.
Unicron optimizes the training process by minimizing failure-related costs
across multiple concurrent tasks within a cluster. Its key features include
in-band error detection for real-time error identification without extra
overhead, a dynamic cost-aware plan generation mechanism for optimal
reconfiguration, and an efficient transition strategy to reduce downtime during
state changes. Deployed on a 128-GPU distributed cluster, Unicron demonstrates
up to a 1.9x improvement in training efficiency over state-of-the-art methods,
significantly reducing failure recovery costs and enhancing the reliability of
large-scale language model training
TouchStone: Evaluating Vision-Language Models by Language Models
Large vision-language models (LVLMs) have recently witnessed rapid
advancements, exhibiting a remarkable capacity for perceiving, understanding,
and processing visual information by connecting visual receptor with large
language models (LLMs). However, current assessments mainly focus on
recognizing and reasoning abilities, lacking direct evaluation of
conversational skills and neglecting visual storytelling abilities. In this
paper, we propose an evaluation method that uses strong LLMs as judges to
comprehensively evaluate the various abilities of LVLMs. Firstly, we construct
a comprehensive visual dialogue dataset TouchStone, consisting of open-world
images and questions, covering five major categories of abilities and 27
subtasks. This dataset not only covers fundamental recognition and
comprehension but also extends to literary creation. Secondly, by integrating
detailed image annotations we effectively transform the multimodal input
content into a form understandable by LLMs. This enables us to employ advanced
LLMs for directly evaluating the quality of the multimodal dialogue without
requiring human intervention. Through validation, we demonstrate that powerful
LVLMs, such as GPT-4, can effectively score dialogue quality by leveraging
their textual capabilities alone, aligning with human preferences. We hope our
work can serve as a touchstone for LVLMs' evaluation and pave the way for
building stronger LVLMs. The evaluation code is available at
https://github.com/OFA-Sys/TouchStone.Comment: https://github.com/OFA-Sys/TouchSton
Gelatin-based biomaterials and gelatin as an additive for chronic wound repair
Disturbing or disrupting the regular healing process of a skin wound may result in its progression to a chronic state. Chronic wounds often lead to increased infection because of their long healing time, malnutrition, and insufficient oxygen flow, subsequently affecting wound progression. Gelatin—the main structure of natural collagen—is widely used in biomedical fields because of its low cost, wide availability, biocompatibility, and degradability. However, gelatin may exhibit diverse tailored physical properties and poor antibacterial activity. Research on gelatin-based biomaterials has identified the challenges of improving gelatin’s poor antibacterial properties and low mechanical properties. In chronic wounds, gelatin-based biomaterials can promote wound hemostasis, enhance peri-wound antibacterial and anti-inflammatory properties, and promote vascular and epithelial cell regeneration. In this article, we first introduce the natural process of wound healing. Second, we present the role of gelatin-based biomaterials and gelatin as an additive in wound healing. Finally, we present the future implications of gelatin-based biomaterials
Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities
We introduce the Qwen-VL series, a set of large-scale vision-language models
designed to perceive and understand both text and images. Comprising Qwen-VL
and Qwen-VL-Chat, these models exhibit remarkable performance in tasks like
image captioning, question answering, visual localization, and flexible
interaction. The evaluation covers a wide range of tasks including zero-shot
captioning, visual or document visual question answering, and grounding. We
demonstrate the Qwen-VL outperforms existing Large Vision Language Models
(LVLMs). We present their architecture, training, capabilities, and
performance, highlighting their contributions to advancing multimodal
artificial intelligence. Code, demo and models are available at
https://github.com/QwenLM/Qwen-VL.Comment: Code, demo and models are available at
https://github.com/QwenLM/Qwen-V
Bis{4,4′-[oxalylbis(azanediyl)]dipyridinium} octamolybdate
In the crystal structure of the title compound, (C12H12N4O2)2[Mo8O26], the amino and pyridinium groups of the N
1,N
2-di(pyridinium-4-yl)oxalamide cations are hydrogen bonded to the O atoms of the centrosymmetric isopolyoxometalate β-[Mo8O26]4− anions, forming a three-dimensional supramolecular architecture
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