287 research outputs found
The Science of Detecting LLM-Generated Texts
The emergence of large language models (LLMs) has resulted in the production
of LLM-generated texts that is highly sophisticated and almost
indistinguishable from texts written by humans. However, this has also sparked
concerns about the potential misuse of such texts, such as spreading
misinformation and causing disruptions in the education system. Although many
detection approaches have been proposed, a comprehensive understanding of the
achievements and challenges is still lacking. This survey aims to provide an
overview of existing LLM-generated text detection techniques and enhance the
control and regulation of language generation models. Furthermore, we emphasize
crucial considerations for future research, including the development of
comprehensive evaluation metrics and the threat posed by open-source LLMs, to
drive progress in the area of LLM-generated text detection
SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes Summarization
Electronic health records (EHRs) store an extensive array of patient
information, encompassing medical histories, diagnoses, treatments, and test
outcomes. These records are crucial for enabling healthcare providers to make
well-informed decisions regarding patient care. Summarizing clinical notes
further assists healthcare professionals in pinpointing potential health risks
and making better-informed decisions. This process contributes to reducing
errors and enhancing patient outcomes by ensuring providers have access to the
most pertinent and current patient data. Recent research has shown that
incorporating prompts with large language models (LLMs) substantially boosts
the efficacy of summarization tasks. However, we show that this approach also
leads to increased output variance, resulting in notably divergent outputs even
when prompts share similar meanings. To tackle this challenge, we introduce a
model-agnostic Soft Prompt-Based Calibration (SPeC) pipeline that employs soft
prompts to diminish variance while preserving the advantages of prompt-based
summarization. Experimental findings on multiple clinical note tasks and LLMs
indicate that our method not only bolsters performance but also effectively
curbs variance for various LLMs, providing a more uniform and dependable
solution for summarizing vital medical information
On determination of the geometric cosmological constant from the OPERA experiment of superluminal neutrinos
The recent OPERA experiment of superluminal neutrinos has deep consequences
in cosmology. In cosmology a fundamental constant is the cosmological constant.
From observations one can estimate the effective cosmological constant
which is the sum of the quantum zero point energy
and the geometric cosmological constant . The
OPERA experiment can be applied to determine the geometric cosmological
constant . It is the first time to distinguish the contributions of
and from each other by experiment. The
determination is based on an explanation of the OPERA experiment in the
framework of Special Relativity with de Sitter space-time symmetry.Comment: 7 pages, no figure
Efficient XAI Techniques: A Taxonomic Survey
Recently, there has been a growing demand for the deployment of Explainable
Artificial Intelligence (XAI) algorithms in real-world applications. However,
traditional XAI methods typically suffer from a high computational complexity
problem, which discourages the deployment of real-time systems to meet the
time-demanding requirements of real-world scenarios. Although many approaches
have been proposed to improve the efficiency of XAI methods, a comprehensive
understanding of the achievements and challenges is still needed. To this end,
in this paper we provide a review of efficient XAI. Specifically, we categorize
existing techniques of XAI acceleration into efficient non-amortized and
efficient amortized methods. The efficient non-amortized methods focus on
data-centric or model-centric acceleration upon each individual instance. In
contrast, amortized methods focus on learning a unified distribution of model
explanations, following the predictive, generative, or reinforcement
frameworks, to rapidly derive multiple model explanations. We also analyze the
limitations of an efficient XAI pipeline from the perspectives of the training
phase, the deployment phase, and the use scenarios. Finally, we summarize the
challenges of deploying XAI acceleration methods to real-world scenarios,
overcoming the trade-off between faithfulness and efficiency, and the selection
of different acceleration methods.Comment: 15 pages, 3 figure
Prevalence of Pediculus Capitis Infestation Among School Children of Chinese Refugees Residing in Mountainous Areas of Northern Thailand
An epidemiologic survey of Pediculus capitis infestation among Akka aboriginal and Han children of Chinese refugees living in mountainous areas at elevations of 1,100 to 1,400 m in Chiang-Rai Province of northern Thailand was conducted during January 2003. Of the 303 children examined, 43 (14.2%) had P. capitis infestation. The overall infestation rate for P. capitis in Akka children (29.3%, 12/41) was significantly higher than that in Han children (11.8%, 31/262; c2 = 8.161, p = 0.002). The prevalence in Akka (52.2%, 12/23) and Han girls (19.7%, 31/157) was higher than that in Akka (0%) and Han boys (0%), respectively (p < 0.001), and the prevalence was higher in Akka girls than in Han girls (c2 = 10.978, p = 0.001). The high prevalence of P. capitis infestation among these girls was possibly due to poor environmental hygiene and unavailability of sufficient water
Towards Assumption-free Bias Mitigation
Despite the impressive prediction ability, machine learning models show
discrimination towards certain demographics and suffer from unfair prediction
behaviors. To alleviate the discrimination, extensive studies focus on
eliminating the unequal distribution of sensitive attributes via multiple
approaches. However, due to privacy concerns, sensitive attributes are often
either unavailable or missing in real-world scenarios. Therefore, several
existing works alleviate the bias without sensitive attributes. Those studies
face challenges, either in inaccurate predictions of sensitive attributes or
the need to mitigate unequal distribution of manually defined non-sensitive
attributes related to bias. The latter requires strong assumptions about the
correlation between sensitive and non-sensitive attributes. As data
distribution and task goals vary, the strong assumption on non-sensitive
attributes may not be valid and require domain expertise. In this work, we
propose an assumption-free framework to detect the related attributes
automatically by modeling feature interaction for bias mitigation. The proposed
framework aims to mitigate the unfair impact of identified biased feature
interactions. Experimental results on four real-world datasets demonstrate that
our proposed framework can significantly alleviate unfair prediction behaviors
by considering biased feature interactions
A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision
Deep learning has the potential to revolutionize sports performance, with
applications ranging from perception and comprehension to decision. This paper
presents a comprehensive survey of deep learning in sports performance,
focusing on three main aspects: algorithms, datasets and virtual environments,
and challenges. Firstly, we discuss the hierarchical structure of deep learning
algorithms in sports performance which includes perception, comprehension and
decision while comparing their strengths and weaknesses. Secondly, we list
widely used existing datasets in sports and highlight their characteristics and
limitations. Finally, we summarize current challenges and point out future
trends of deep learning in sports. Our survey provides valuable reference
material for researchers interested in deep learning in sports applications
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