Comparing Fine-Tuning, Zero and Few-Shot Strategies with Large Language Models in Hate Speech Detection in English

Large Language Models(LLMs)are increasingly demonstrating their ability to understand natural language and solve complex tasks,especially through text generation.One of the relevant capabilities is contextual learning,which involves the ability to receive instructions in natural language or task demonstrations to generate expected outputs for test instances without the need for additional training or gradient updates.In recent years,the popularity of social networking has provided a medium through which some users can engage in offensive and harmful online behavior.In this study,we investigate the ability of different LLMs,ranging from zero-shot and few-shot learning to fine-tuning.Our experiments show that LLMs can identify sexist and hateful online texts using zero-shot and few-shot approaches through information retrieval.Furthermore,it is found that the encoder-decoder model called Zephyr achieves the best results with the fine-tuning approach,scoring 86.811%on the Explainable Detection of Online Sexism(EDOS)test-set and 57.453%on the Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter(HatEval)test-set.Finally,it is confirmed that the evaluated models perform well in hate text detection,as they beat the best result in the HatEval task leaderboard.The error analysis shows that contextual learning had difficulty distinguishing between types of hate speech and figurative language.However,the fine-tuned approach tends to produce many false positives.

Fine-tuning electronic structure of N-doped graphitic carbon-supported Co-and Fe-incorporated Mo_(2)C to achieve ultrahigh electrochemical water oxidation activity

Mo_(2)C is an excellent electrocatalyst for hydrogen evolution reaction(HER).However,Mo_(2)C is a poor electrocatalyst for oxygen evolution reaction(OER).Herein,two different elements,namely Co and Fe,are incorporated in Mo_(2)C that,therefore,has a finely tuned electronic structure,which is not achievable by incorporation of any one of the metals.Consequently,the resulting electrocatalyst Co_(0.8)Fe_(0.2)-Mo_(2)C-80 displayed excellent OER catalytic performance,which is evidenced by a low overpotential of 214.0(and 246.5)mV to attain a current density of 10(and 50)mA cm^(-2),an ultralow Tafel slope of 38.4 mV dec^(-1),and longterm stability in alkaline medium.Theoretical data demonstrates that Co_(0.8)Fe_(0.2)-Mo_(2)C-80 requires the lowest overpotential(1.00 V)for OER and Co centers to be the active sites.The ultrahigh catalytic performance of the electrocatalyst is attributed to the excellent intrinsic catalytic activity due to high Brunauer-Emmett-Teller specific surface area,large electrochemically active surface area,small Tafel slope,and low chargetransfer resistance.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

Optimizing Enterprise Conversational AI: Accelerating Response Accuracy with Custom Dataset Fine-Tuning

As the realm of enterprise-level conversational AI continues to evolve, it becomes evident that while generalized Large Language Models (LLMs) like GPT-3.5 bring remarkable capabilities, they also bring forth formidable challenges. These models, honed on vast and diverse datasets, have undoubtedly pushed the boundaries of natural language understanding and generation. However, they often stumble when faced with the intricate demands of nuanced enterprise applications. This research advocates for a strategic paradigm shift, urging enterprises to embrace a fine-tuning approach as a means to optimize conversational AI. While generalized LLMs are linguistic marvels, their inability to cater to the specific needs of businesses across various industries poses a critical challenge. This strategic shift involves empowering enterprises to seamlessly integrate their own datasets into LLMs, a process that extends beyond linguistic enhancement. The core concept of this approach centers on customization, enabling businesses to fine-tune the AI’s functionality to fit precisely within their unique business landscapes. By immersing the LLM in industry-specific documents, customer interaction records, internal reports, and regulatory guidelines, the AI transcends its generic capabilities to become a sophisticated conversational partner aligned with the intricacies of the enterprise’s domain. The transformative potential of this fine-tuning approach cannot be overstated. It enables a transition from a universal AI solution to a highly customizable tool. The AI evolves from being a linguistic powerhouse to a contextually aware, industry-savvy assistant. As a result, it not only responds with linguistic accuracy but also with depth, relevance, and resonance, significantly elevating user experiences and operational efficiency. In the subsequent sections, this paper delves into the intricacies of fine-tuning, exploring the multifaceted challenges and abundant opportunities it presents. It addresses the technical intricacies of data integration, ethical considerations surrounding data usage, and the broader implications for the future of enterprise AI. The journey embarked upon in this research holds the potential to redefine the role of conversational AI in enterprises, ushering in an era where AI becomes a dynamic, deeply relevant, and highly effective tool, empowering businesses to excel in an ever-evolving digital landscape.

New approach to assess sperm DNA fragmentation dynamics： Fine-tuning mathematical models

Background： Sperm DNA fragmentation（sDF） has been proved to be an important parameter in order to predict in vitro the potential fertility of a semen sample. Colloid centrifugation could be a suitable technique to select those donkey sperm more resistant to DNA fragmentation after thawing. Previous studies have shown that to elucidate the latent damage of the DNA molecule, sDF should be assessed dynamically, where the rate of fragmentation between treatments indicates how resistant the DNA is to iatrogenic damage. The rate of fragmentation is calculated using the slope of a linear regression equation. However, it has not been studied if s DF dynamics fit this model. The objectives of this study were to evaluate the effect of different after-thawing centrifugation protocols on sperm DNA fragmentation and elucidate the most accurate mathematical model（linear regression, exponential or polynomial） for DNA fragmentation over time in frozen-thawed donkey semen.Results： After submitting post-thaw semen samples to no centrifugation（UDC）, sperm washing（SW） or single layer centrifugation（SLC） protocols, sD F values after 6 h of incubation were significantly lower in SLC samples than in SW or UDC.Coefficient of determination（R-2） values were significantly higher for a second order polynomial model than for linear or exponential. The highest values for acceleration of fragmentation（aSDF） were obtained for SW, fol owed by SLC and UDC.Conclusion： SLC after thawing seems to preserve longer DNA longevity in comparison to UDC and SW. Moreover,the fine-tuning of models has shown that sDF dynamics in frozen-thawed donkey semen fit a second order polynomial model, which implies that fragmentation rate is not constant and fragmentation acceleration must be taken into account to elucidate hidden damage in the DNA molecule.

Aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders:progress of experimental models based on disease pathogenesis

Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

A promising approach for quantifying focal stroke modeling and assessing stroke progression:optical resolution photoacoustic microscopy photothrombosis

To investigate the mechanisms underlying the onset and progression of ischemic stroke,some methods have been proposed that can simultaneously monitor and create embolisms in the animal cerebral cortex.However,these methods often require complex systems and the effect of age on cerebral embolism has not been adequately studied,although ischemic stroke is strongly age-related.In this study,we propose an optical-resolution photoacoustic microscopy-based visualized photothrombosis methodology to create and monitor ischemic stroke in mice simultaneously using a 532 nm pulsed laser.We observed the molding process in mice of different ages and presented age-dependent vascular embolism differentiation.Moreover,we integrated optical coherence tomography angiography to investigate age-associated trends in cerebrovascular variability following a stroke.Our imaging data and quantitative analyses underscore the differential cerebrovascular responses to stroke in mice of different ages,thereby highlighting the technique's potential for evaluating cerebrovascular health and unraveling age-related mechanisms involved in ischemic strokes.

Reduced mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor contributes to neurodegeneration in a model of spinal and bulbar muscular atrophy pathology

Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen receptor protein,characterized by polyglutamine expansion,is prone to misfolding and forms aggregates in both the nucleus and cytoplasm in the brain in spinal and bulbar muscular atrophy patients.These aggregates alter protein-protein interactions and compromise transcriptional activity.In this study,we reported that in both cultured N2a cells and mouse brain,mutant androgen receptor with polyglutamine expansion causes reduced expression of mesencephalic astrocyte-de rived neurotrophic factor.Overexpressio n of mesencephalic astrocyte-derived neurotrophic factor amelio rated the neurotoxicity of mutant androgen receptor through the inhibition of mutant androgen receptor aggregation.Conversely.knocking down endogenous mesencephalic astrocyte-derived neurotrophic factor in the mouse brain exacerbated neuronal damage and mutant androgen receptor aggregation.Our findings suggest that inhibition of mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor is a potential mechanism underlying neurodegeneration in spinal and bulbar muscular atrophy.

采用STAMP-24Model的多组织事故分析

安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事故分析的方法,并以青岛石油爆炸事故为例进行事故原因分析。结果显示:STAMP-24Model可以分组织,分层次且有效、全面、详细地分析涉及多个组织的事故原因,探究多组织之间的交互关系;对事故进行动态演化分析,可得到各组织不安全动作耦合关系与形成的事故失效链及管控失效路径,进而为预防多组织事故提供思路和参考。

基于改进24Model-ISM-SNA建筑工人不安全行为关联路径研究

建筑施工现场环境复杂,为有效控制不安全行为发生,基于行为安全“2-4”模型对360份具有代表性的建筑安全事故调查报告进行分析,提取出22个不安全行为的主要影响因素。利用灰色关联分析方法(GRA)改进的集成ISM-SNA模型,将不安全行为风险因素划分为表层、过渡层与深层,然后对风险因素进行可视化分析、中心度分析及凝聚子群分析,揭示了各致因因素间的关联关系和传导路径。结果表明,建筑工人不安全行为影响因素可划分成7级3阶的多级递阶结构,安全意识、现场监管、外部环境是建筑工人不安全行为的关键影响因素,同时现场监管和隐患排查到位能有效降低不安全行为的发生。

基于24Model的地铁内涝事故原因分析与评估

为降低地铁内涝事故灾害风险,基于事故致因“2-4”模型,分析了地铁内涝事故致灾因子,采用层次分析法构建了地铁内涝事故原因分析指标体系,确定了各风险因子的权重,并利用模糊综合评价法对地铁内涝事故进行了定量评估,识别出关键的影响因素。结果表明:地铁内涝事故一级指标中不安全动作与物态因素最重要,其中影响最大的指标包括擅自更改建筑设计、未按照要求检查水位情况、未及时排水、出入口不符合防汛标准等因素;习惯性不安全行为的权重位居第二,表明该指标因素较为重要,同时安全管理体系得分位居第二,表明该指标因素较易发生。对关键指标采取防范措施,可有效降低风险,从而减少地铁内涝事故的发生。

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。

Rotary-scaling fine-tuning (RSFT) method for optimizing railway wheel profiles and its application to a locomotive

The existing multi-objective wheel profile optimization methods mainly consist of three sub-modules:(1)wheel profile generation,(2)multi-body dynamics simulation,and(3)an optimization algorithm.For the first module,a comparably conservative rotary-scaling finetuning(RSFT)method,which introduces two design variables and an empirical formula,is proposed to fine-tune the traditional wheel profiles for improving their engineering applicability.For the second module,for the TRAXX locomotives serving on the Blankenburg–Rubeland line,an optimization function representing the relationship between the wheel profile and the wheel–rail wear number is established based on Kriging surrogate model(KSM).For the third module,a method combining the regression capability of KSM with the iterative computing power of particle swarm optimization(PSO)is proposed to quickly and reliably implement the task of optimizing wheel profiles.Finally,with the RSFT–KSM–PSO method,we propose two wear-resistant wheel profiles for the TRAXX locomotives serving on the Blankenburg–Rubeland line,namely S1002-S and S1002-M.The S1002-S profile minimizes the total wear number by 30%,while the S1002-M profile makes the wear distribution more uniform through a proper sacrifice of the tread wear number,and the total wear number is reduced by 21%.The quasi-static and hunting stability tests further demonstrate that the profile designed by the RSFT–KSM–PSO method is promising for practical engineering applications.

An Intelligent Fine-Tuned Forecasting Technique for Covid-19 Prediction Using Neuralprophet Model

COVID-19,being the virus of fear and anxiety,is one of the most recent and emergent of various respiratory disorders.It is similar to the MERS-COV and SARS-COV,the viruses that affected a large population of different countries in the year 2012 and 2002,respectively.Various standard models have been used for COVID-19 epidemic prediction but they suffered from low accuracy due to lesser data availability and a high level of uncertainty.The proposed approach used a machine learning-based time-series Facebook NeuralProphet model for prediction of the number of death as well as confirmed cases and compared it with Poisson Distribution,and Random Forest Model.The analysis upon dataset has been performed considering the time duration from January 1st 2020 to16th July 2021.The model has been developed to obtain the forecast values till September 2021.This study aimed to determine the pandemic prediction of COVID-19 in the second wave of coronavirus in India using the latest Time-Series model to observe and predict the coronavirus pandemic situation across the country.In India,the cases are rapidly increasing day-by-day since mid of Feb 2021.The prediction of death rate using the proposed model has a good ability to forecast the COVID-19 dataset essentially in the second wave.To empower the prediction for future validation,the proposed model works effectively.

Railway wheel profile fine-tuning system for profile recommendation

This paper develops a wheel profile fine-tuning system(WPFTS)that comprehensively considers the influence of wheel profile on wheel damage,vehicle stability,vehicle safety,and passenger comfort.WPFTS can recommend one or more optimized wheel profiles according to train operators’needs,e.g.,reducing wheel wear,mitigating the development of wheel out-of-roundness(OOR),improving the shape stability of the wheel profile.Specifically,WPFTS includes four modules:(I)a wheel profile generation module based on the rotary-scaling finetuning(RSFT)method;(II)a multi-objective generation module consisting of a rigid multi-body dynamics simulation(MBS)model,an analytical model,and a rigid–flexible MBS model,for generating 11 objectives related to wheel damage,vehicle stability,vehicle safety,and passenger comfort;(III)a weight assignment module consisting of an adaptive weight assignment strategy and a manual weight assignment strategy;and(IV)an optimization module based on radial basis function(RBF)and particle swarm optimization(PSO).Finally,three cases are introduced to show how WPTFS recommends a wheel profile according to train operators’needs.Among them,a wheel profile with high shape stability,a wheel profile for mitigating the development of wheel OOR,and a wheel profile considering hunting stability and derailment safety are developed,respectively.

24Model与LCM原因因素定义对比研究

为探究损失致因模型(LCM)原因因素定义与事故致因“2-4”模型(24Model)存在的异同和优缺点,梳理2个模型各层面原因和结果的定义,对比定义内容及其对事故原因分析等安全实务的指导作用,并以一起瓦斯爆炸事故为例加以实证分析,获得二者分析结果之间的差异。研究结果表明:LCM是首个将管理因素纳入事故致因分析的一维事件序列模型,可明确各层面原因因素的定义和因素间的逻辑关系,但部分定义存在交叉重复的问题,并没有揭示安全工作指导思想等深层次事故致因因素;24Model作为系统性事故致因模型,对各类因素的定义均以组织为主体,描述事件、事故、安全的概念内涵,划分个体安全动作、安全能力和组织安全管理体系的类别并给出含义解析,探究组织安全文化层面的问题并以32个元素体现;2个模型的事故原因分析方法均建立在对各层级原因因素定义的基础上,并适用于模型理论体系本身。

Anisotropic time-dependent behaviors of shale under direct shearing and associated empirical creep models

Understanding the anisotropic creep behaviors of shale under direct shearing is a challenging issue.In this context,we conducted shear-creep and steady-creep tests on shale with five bedding orientations (i.e.0°,30°,45°,60°,and 90°),under multiple levels of direct shearing for the first time.The results show that the anisotropic creep of shale exhibits a significant stress-dependent behavior.Under a low shear stress,the creep compliance of shale increases linearly with the logarithm of time at all bedding orientations,and the increase depends on the bedding orientation and creep time.Under high shear stress conditions,the creep compliance of shale is minimal when the bedding orientation is 0°,and the steady-creep rate of shale increases significantly with increasing bedding orientations of 30°,45°,60°,and 90°.The stress-strain values corresponding to the inception of the accelerated creep stage show an increasing and then decreasing trend with the bedding orientation.A semilogarithmic model that could reflect the stress dependence of the steady-creep rate while considering the hardening and damage process is proposed.The model minimizes the deviation of the calculated steady-state creep rate from the observed value and reveals the behavior of the bedding orientation's influence on the steady-creep rate.The applicability of the five classical empirical creep models is quantitatively evaluated.It shows that the logarithmic model can well explain the experimental creep strain and creep rate,and it can accurately predict long-term shear creep deformation.Based on an improved logarithmic model,the variations in creep parameters with shear stress and bedding orientations are discussed.With abovementioned findings,a mathematical method for constructing an anisotropic shear creep model of shale is proposed,which can characterize the nonlinear dependence of the anisotropic shear creep behavior of shale on the bedding orientation.

Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.