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.

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.

The Research on Cross-Cultural Critical Thinking in English Teaching: A Pedagogical Paradigm Within the Ideological and Political Course Framework

The conflation of linguistic competencies with ideological and political educational paradigms presents unique opportunities and challenges for English language teaching(ELT)in our increasingly interconnected world.This research seeks to address the role of cross-cultural critical thinking within the landscape of English pedagogy,framed by the imperatives of ideological and political education.Employing a multi-method approach,including thematic content analysis and action research,the study proposes a pedagogical model designed to integrate transcultural discourse and ideologically reflective practices into the conventional ELT curriculum.This paper argues for a reimagined approach to teaching English that encourages students to engage with diverse cultural perspectives and develop a nuanced understanding of global issues.

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.

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.

DAO to HANOI via DeSci: AI Paradigm Shifts from AlphaGo to ChatGPT

From AlphaGo to ChatGPT,the field of AI has launched a series of remarkable achievements in recent years.Analyzing,comparing,and summarizing these achievements at the paradigm level is important for future AI innovation,but has not received sufficient attention.In this paper,we give an overview and perspective on machine learning paradigms.First,we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge perspective.Accordingly,we give an overview on three basic and twelve extended learning paradigms,such as Ensemble Learning,Transfer Learning,etc.,with figures in unified style.We further analyze three advanced paradigms,i.e.,AlphaGo,AlphaFold and ChatGPT.Second,to enable more efficient and effective scientific discovery,we propose to build a new ecosystem that drives AI paradigm shifts through the decentralized science(DeSci)movement based on decentralized autonomous organization(DAO).To this end,we design the Hanoi framework,which integrates human factors,parallel intelligence based on a combination of artificial systems and the natural world,and the DAO to inspire AI innovations.

Fine-tuning of cortical progenitor proliferation by thalamic afferents

During cerebral cortical cortex neurogenesis two major types of progenitors generate a variety of morphologically and functionally diverse projection neurons destined for the different cortical layers in non-gyrified mice. Radial glia cells （RGCs） undergo mitosis in the cortical ventricular zone and exhibit an apical-basal cell polarity, whereas non-polar intermediate progenitor cells （IPCs） divide basally in the subventricular zone （Franco and Muller, 2013; Taverna et al., 2014）.

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.

Fine-Tuning Bilateral Ties

Chinese Vice Premier’s visit to Africa continues to emphasize the mutual cooperation,with a focus on agriculture FOR many years,the Chinese Government has dispatched the minister of foreign affairs to Africa for the first official visit of a year.This year,however,that rule was broken when Hui Liangyu,Chinese Vice Premier,made the 14-day trip. On January 6-19,Hui paid official visits to Mauritius,Zambia,the Democratic Republic of Congo(DRC),Cameroon and Senegal,focusing on economic and agri-

William Morris Davis: Father of Geomorphology or Father of Geology’s Unrecognized Paradigm Problem

An often unrecognized problem is the geology and glacial history paradigm’s inability to explain topographic map drainage system and erosional landform evidence, which means geology research studies rarely address that type of topographic map evidence. The problem originated in the late 19th century with William Morris Davis who is sometimes called the father of geomorphology and was one of the first geologists to interpret what in the late 19th century were newly available topographic maps. An 1889 Davis paper describes selected drainage system evidence observed on an advance copy of the 1890 Doylestown (Pennsylvania) topographic map and an 1892 Ward paper written after discussions with Davis describes additional selected drainage system evidence seen on the same map. Both papers fail to mention the majority of the Doylestown map’s drainage system features including most barbed tributaries, asymmetric drainage divides, and through (dry) valleys crossing major drainage divides. Had Davis used all of the map’s drainage system and erosional landform evidence he should have recognized the map evidence shows headward erosion of an east-oriented Neshaminy Creek valley captured southwest-oriented streams which headward erosion of the south-oriented Delaware River valley and its east-oriented tributary Tohickon Creek valley had beheaded. Consciously or unconsciously, Davis chose not to alert future investigators that Doylestown topographic map evidence did not support his yet-to-be-published Pennsylvania and New Jersey erosion history interpretations and instead Davis proceeded to develop and promote erosion history interpretations which the map evidence did not support.

Locomotion Paradigms of Topological Space in Shakespeare’s Double Falsehood

In the 16th and 17th century,Britain was constantly strained by sporadic plagues,famines,fires and political conflicts.As a response in literature,Shakespeare has constructed an annular locomotion paradigm in Fletcher and his collaboration work Cardenio.Be more specific,the locomotion routes can be exemplified in Möbius strip,a rhizome system,a reciprocating juxtaposition between the foreground and the background,either of which finally runs to an annular schema.The annular in narration corresponds with The Globe theater and the round world.Shakespeare may express his expectation and prospect for the uprising bourgeoise and the newly found world by the resurrection theme in literature.

法治现代化的“共同特征”和“中国特色”

“中国式法治现代化”这一概念提出的理论和实践依据在于:中国式法治现代化是中国式现代化的内在组成部分;法治现代化是现代化的核心要素和重要标志;中国式法治现代化是对中国式法治现代化实践的理论表达和学理表征。世界各国法治现代化的“共同特征”包括宪法至上、尊重和保障人权、监督和制约公权、维护公平正义、构建社会秩序、促进人类和平、立足本国国情、法治文明互鉴。中国式法治现代化的特色在于坚持中国共产党领导、以人民为中心、全面依法治国、大国法治、赓续中华法治文明、面向全球化。共同特征和中国特色的完美结合构成了法治现代化的中国范式。

智能时代人因科学研究的新范式取向及重点

本文首先提出了“人因科学”这一创新的学科群概念来表征工程心理学、人因工程、工效学、人机交互等相近领域。尽管这些领域的研究角度不一样,但是它们分享共同的研究理念、对象以及目的。我们近期的研究表明,人工智能(AI)新技术带来了一系列新的人因问题,而作为人因科学研究对象的人机关系呈现出从“人机交互”向“人智组队式合作”的跨时代演进。这些变化对人因科学研究提出了新问题和新挑战,需要我们重新审视基于非智能技术的人因科学研究范式和重点。在此背景下,本文梳理人因科学研究范式取向跨时代的演进,总结我们近5年所提出的一系列用于丰富人因科学研究范式的新概念模型和框架,其中包括人智协同认知系统、人智协同认知生态系统以及智能社会技术系统的模型和框架。本文进一步从人因科学研究范式取向的角度进一步提升这些概念模型和框架,提出智能时代人因科学研究的三种新范式取向,分析相应的应用意义,并展望今后的研究方向。同时,针对智能时代人因科学研究重点的跨时代转移新特征,本文从“人智交互”“智能人机界面”“人智组队合作”三个方面展望了今后人因科学的研究重点,揭示出人因科学新研究范式取向对未来研究重点的作用。我们认为,人因科学的研究范式取向和研究重点互为影响,互为促进,智能时代的人因科学研究需要多样化、创新的研究范式取向,从而进一步推动人因科学的发展。

数字化转型背景下课堂教学范式重构

课堂教学的数字化转型,是将课堂中的各种教学要素信息化,教学信息资源化,进而促进师生高效利用信息化资源,实现课堂教学育人方式的转变与教学质量的提升。数字化转型将推动课堂教学范式重构。传统课堂是“信息呈现”教学范式,用压抑学生学情信息来保障教师教学信息的质量。学情信息是课堂教学数字化转型的生长点,充分发挥其价值,需利用“任务设计”“结构变革”“学习地图”等举措,建构以发现和利用学情为旨趣的“信息引导”教学范式。在数字化的技术赋能下,“信息引导”教学范式将走向“精准教学”,塑造人才培养的全新课堂样态。

“双碳”目标下的经济社会系统性变革:特征、要求与路径

实现碳达峰碳中和是一场广泛而深刻的经济社会系统性变革,强调单一目标或有限目标调控的研究范式,对碳中和目标下净零碳转型复杂性的理解存在局限性,难以有效反映与应对多重目标、多重约束下社会经济系统性、全局性深刻变革的影响。理解与研判“双碳”目标下经济社会系统性变革的主要特征、具体要求与实现路径,能够提升对“双碳”目标的认识,更好地释放系统性变革带来的红利以及规避所伴随的风险,推动实现“双碳”目标下经济社会的高质量发展,并为全球碳中和理论构建与实践探索作出贡献。

广义安全的本体、视界与范式:“关系和合度”解析

基于中国关系本体论对安全的本体和视界进行解析,并且建构中国的安全理论范式,是为全球安全治理提供中国方案的重要方面。安全是一种什么样的“实在”,这是十分重要的本体论问题;而以什么样的“视界”考察安全,则是非常棘手的认识论难题。“关系和合度”解析为“广义安全论”视域下的安全本体、视界与范式提供了哲学诠释。安全是“关系性实在”,是和合状态的共享性秩序,是呈现人与世界“广义性联系”的“关系和合度”。如果关系的和合程度是安全获得的程度,那么安全就是以“关系和合度”为自变量的函数,广义安全便是呈现人与世界“关系和合度”的安全,是“行为体间的优态共存”。广义安全凸显“关系和合度”的适然性,安全之境即是保持优态共存的“适然之境”,是当下所发生的一切关系的总体性和合,这为总体国家安全观以及全球安全观提供了理论支撑。广义安全观是集成与整合诸安全构成要素与安全领域的大安全理念,是“场域安全”思维的完好体现,是多重时空关系状态与多种活动性质特点的融合。

领域法学视野下的数字法问题

数字法律现象呈现出客体复合性、主体多样性及法律关系多元性特征,决定了传统研究范式转型升级成为数字法学的必然选择。领域法学作为调整交叉领域法现象的研究范式,与数字法问题相吻合。领域法学具体化、立体化以及复合化思维,可与部门法学协同互补;其立体性、自足性和因应性特征可对传统法学研究方法进行吸收和补充。运用领域法学研究范式,可立体化提炼数字法现象,多维度整合数字交叉学科,以达到全方位解决数字法问题的效果。在宏观上,归纳“数字赋权-代码规制-数字正义”的数字治理逻辑;在微观上,演绎数据权利分类确定、数据交易分层规范、算法治理多元共治、权益救济多方协同的数字治理机制,构建完整的数字法学知识体系。

基于“四个面向”的高校“有组织科研”范式构建--一个单案例研究

当今世界正经历百年未有之大变局,世界各国科技竞争加剧,科技创新成为各国博弈的主战场。我国在基础研究、关键核心技术等领域正在遭受西方发达国家的围堵。高校作为基础研究主力军和重大科技突破策源地,传统科研范式在响应国家重大需求方面存在原始创新能力薄弱、科技成果转化率低、协同创新能力不足等问题。持续增大的外部压力、创新高风险与高投入使得“有组织科研”成为当前我国高校提升创新效能的重要选择,也是高校深度融入国家创新体系的重要举措。阐述“四个面向”和“有组织科研”的内涵,梳理“四个面向”与“有组织科研”、“有组织科研”与自由探索的关系,从高校传统科研范式亟待解决的问题入手,构建基于“四个面向”的高校“有组织科研”范式,对重庆大学“有组织科研”实施路径与成效进行解析,验证理论的科学性与适用性。

医学伦理学发展新阶段的历史使命

每个时代都有每个时代伦理学的主轴。当今医学伦理学面临崭新的境遇,资本、技术、权力和全民健康等问题,已经构成医学伦理学应对和挑战的主要课题。新形势向医学伦理学提出了种种新任务,探索完成这些新任务的政策建议和伦理举措;适应新形势医学伦理学如何加强自身理念的充实、创新和提高;在新情境下如何促进医学伦理学在医学领域内的全面覆盖,让医学伦理学在医学中深深扎根,促进医学技术更好地服务于人民大众的健康,都是开启医学伦理学新征程面临的担当。

“双碳”目标下国土空间优化的挑战与应对

国土空间优化与调控始终是现代化建设面临的重大理论和实践问题。现有的国土空间规划研究多聚焦于对低碳空间治理思路、框架、工具的整体性把握,尚缺乏明确的碳约束目标及行动指南,亟待将“双碳”目标全面融入国土空间规划的目标体系、决策体系和保障体系,发挥规划在空间治理上的统筹引领作用。首先,国土空间优化光论“地”不谈“天”不行,也不能“就土论土、就地论地”;其次,低碳和“双碳”目标并不完全等同,要避免“双碳”研究中忽视国土,也要在研究国土问题中厘清“双碳”;再次,空间优化是个复杂动态过程,需要高度重视深度不确定性。因此,未来有必要建立国土空间抗解性识别分析范式和方法,开发应对深度不确定性的国土空间协同演化集成模型,构建国土空间多目标优化调控理论体系,并沿着科学认知、信息技术、智能规划、创新制度四个主轴展开深入研究和理论构建。