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.

Mathematical Foundation of Visual Query Languageon Spatial Information

Visual Query Language on Spatial Information (SIVQL) is one kind of visual query language based on the extension of Query by Example (QBE). It is a visual operation based on graphics or media object, such as point, line and area elements. In this paper, the relation calculation and query function of SIVQL have been studied and discussed by using set theory and relation algebra. The theory foundation of SIVQL has been investigated by the mathematical method. Finally, its application examples are also given with the specific information system.

The Effect of Using L1 （Arabic Language） in the L2 （English Language） Classroom on the Achievement in General English of Foundation Year Students in King Abdulaziz University

This study aims at investigating the effect of using L 1 （Arabic Language） while teaching a target language （English Language） on the achievement in General English of foundation year students in King Abdulaziz University. To achieve the purpose of the study, the researcher used an experimental design： an experimental group and a control group. The independent variable is using L1 while teaching English in very limited and specified areas. The dependent variable is students＇ achievement in general English. The statistics used is the t-test. The population of the study was all students enrolled in the foundation year 1431/1432 in the ELI （English Language Institute） at King Abdulaziz University. The sample of the study consisted of 50 students taking North Star in the sections A and B as a university requirement in the foundation year 1431/1432 in King Abdulaziz University. The results of the study were in favor of banning Arabic in the English language classroom as shown in the mean scores of the control and experimental groups in the tables. It is recommended that teachers and instructors should be trained to use teaching strategies that help them use English only in the English language classroom.

The Study of English Negative Transfer to French Language Learning with French Majors

English language(EL) and French language(FL) are very similar in their language attributes-lexicon,syntax and tenses.During FL acquisition,especially during the early acquisition stages,FL learners are strongly influenced by their EL forms.The essential point of this paper is to distinguish the differences and similarities of the EL and FL,then to suggest some strategies to restrain the negative transfer effectively,which would be helpful in third language acquisition.

The Study of English Negative Transfer to French Language Learning with French Majors

English language(EL) and French language(FL) are very similar in their language attributes-lexicon,syntax and tenses.During FL acquisition,especially during the early acquisition stages,FL learners are strongly influenced by their EL forms.The essential point of this paper is to distinguish the differences and similarities of the EL and FL,then to suggest some strategies to restrain the negative transfer effectively,which would be helpful in third language acquisition.

Automated Design and Visualization System of a House Foundation from Blocks

This article analyzes creation methods of automated design system, presents design system of a house foundation from blocks. The creation methods of automated design system of a house foundation from blocks are discovered with Unified Modeling Language. Analyzed objects-classes： block, specification, model. Graphical system can design foundation, form specification of objects and create 3D model of house foundation. There are several types and different dimensions of concrete blocks. The program optimally arranges selected blocks so that monolithic parts will be minimal volume. Program selects a house foundation blocks from database by using ActiveX Data Objects technology, which by programming method connects drawing and database. Drawing＇s graphical objects have additional data from which exchange of data between graphical system and database is executed. Visualization system and example of house foundation from blocks project with specifications is presented. Creation problems of automated design system are discussed and conclusions are made.

Stereotypes in a Foreign Language Classroom --Modifying Negative Attitudes to Enhance Foreign Language Learning

The aim of the paper is to present various aspects of the phenomenon of stereotyping in the context of FL （foreign language） learning and teaching and to discuss practical solutions to be used in a FL classroom to teach the worm about the worm by questioning the stereotypes learners have of other nations and languages. This paper is an attempt to present some ideas of FL teachers＇ role in developing students＇ socio-cultural competence with the aim of raising their cross-cultural awareness and questioning the stereotypes students bring into a FL classroom. The methodology used was an analysis of fragment of tape scripts from listening comprehension activities from a course book preparing Polish secondary students for the school leaving exam. The topics discussed concern opinions about attitudes towards and judgments of various cultural aspects, be it drinking tea or discussing the weather, impressions people have about other nations, or languages people speak.

Anxiety and Motivation in Foreign Language Achievement

Language anxiety and language motivation have been studied extensively by experts in foreign language （FL） learning. Both of these constructs have been found to be closely correlated to FL achievement. In this article, the author has reviewed foreign language anxiety, language motivation and the relationship between language motivation and language anxiety.

革命传统“进高考”的理据、价值与原则——以高考语文为例

推动革命传统教育加快进入新课程、新教材,需要与各个学科评价的内在需求相结合,在高考内容改革中有所反映。以高考语文科为例展开讨论,在明晰革命传统“进高考”的政策和学理依据基础上,提出高考语文融入革命传统教育内容,具有为落实立德树人根本任务提供有效载体、重构革命文化融入课程体系的行动路径、实现革命文化思想内涵与情感价值的具象建构等独特的价值意蕴,进而提出以侧重缘文悟道、注重思维考查、引导知行统一为“进高考”的三大基本原则。

从基础智能到通用智能:基于大模型的GenAI和AGI之现状与展望

本文对生成式AI(Generative artificial intelligence,GenAI)的国内外发展现状进行了概述,重点分析了中美之间在算力、数据、算法、生态等方面存在的差距.为改变我国在生成式AI领域的落后现状,提出高能效算力建设、联邦数据、专业领域模型、基于TAO的联邦生态等应对策略,对大模型时代AI安全治理进行了论述,对通用人工智能(Artificial general intelligence,AGI)的未来发展进行了展望.

油气行业人工智能大模型应用研究现状及展望

阐述了大模型技术的概念,总结了大模型技术的国内外研究现状,综述了大模型在垂直领域的应用现状,梳理了油气行业大模型应用面临的挑战,并对油气行业大模型应用进行了展望。现有大模型可粗略分为3类,即大语言模型、视觉大模型和多模态大模型。油气行业大模型应用刚刚起步,部分油气企业基于开源大语言模型,利用微调、检索增强等方式发布大语言模型产品,部分学者尝试利用视觉/多模态基础模型研发面向油气业务的场景模型,还有少数学者构建地震资料处理解释、岩心分析等领域的预训练基础模型。油气行业大模型应用面临数据量和数据质量难以支撑大模型训练、研发投入成本高、难以实现算法自主可控等挑战。油气行业在应用大模型时应始终聚焦油气主营业务需求,以大模型应用为契机,加强数据全生命周期管理,提升数据治理能力,推动融合算力建设,加强“人工智能+能源”复合团队建设,推动大模型技术自主可控。

A reliable knowledge processing framework for combustion science using foundation models

This research explores the integration of large language models (LLMs) into scientific data assimilation, focusing on combustion science as a case study. Leveraging foundational models integrated with Retrieval-Augmented Generation (RAG) framework, the study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The multifaceted nature of combustion research emphasizes the critical role of knowledge processing in navigating and extracting valuable information from a vast and diverse pool of sources. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. It incorporates prompt engineering and offline open-source LLMs, offering user autonomy in selecting base models. The study provides a thorough examination of text segmentation strategies, conducts comparative studies between LLMs, and explores various optimized prompts to demonstrate the effectiveness of the framework. By incorporating an external vector database, the framework outperforms a conventional LLM in generating accurate responses and constructing robust arguments. Additionally, the study delves into the investigation of optimized prompt templates for the purpose of efficient extraction of scientific literature. Furthermore, we present a targeted scaling study to quantify the algorithmic performance of the framework as the number of prompt tokens increases. The research addresses concerns related to hallucinations and false research articles by introducing a custom workflow developed with a detection algorithm to filter out inaccuracies. Despite identified areas for improvement, the framework consistently delivers accurate domain-specific responses with minimal human oversight. The prompt-agnostic approach introduced holds promise for future improvements. The study underscores the significance of integrating LLMs and knowledge processing techniques in scientific research, providing a foundation for advancements in data assimilation and utilization.

Opportunities for large language models and discourse in engineering

In recent years, large language models have achieved breakthroughs on a wide range of benchmarks in natural language processing and continue to increase in performance. Recently, the advances of large language models have raised interest outside the natural language processing community and could have a large impact on daily life. In this paper, we pose the question: How will large language models and other foundation models shape the future product development process? We provide the reader with an overview of the subject by summarizing both recent advances in natural language processing and the use of information technology in the engineering design process. We argue that discourse should be regarded as the core of engineering design processes, and therefore should be represented in a digital artifact. On this basis, we describe how foundation models such as large language models could contribute to the design discourse by automating parts thereof that involve creativity and reasoning, and were previously reserved for humans. We describe how simulations, experiments, topology optimizations, and other process steps can be integrated into a machine-actionable, discourse-centric design process. As an example, we present a design discourse on the optimization of wind turbine blades. Finally, we outline the future research that will be necessary for the implementation of the conceptualized framework.

Research status and application of artificial intelligence large models in the oil and gas industry

This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology.

基于联邦分割学习与低秩适应的RoBERTa预训练模型微调方法

微调后的大语言模型(Large language models,LLMs)在多任务中表现出色,但集中式训练存在用户隐私泄漏的风险。联邦学习(Federated learning,FL)通过本地训练避免了数据共享,但LLMs庞大的参数量对资源受限的设备和通信带宽构成挑战,导致在边缘网络中部署困难。结合分割学习(Split learning,SL),联邦分割学习可以有效解决这一问题。基于模型深层权重的影响更为显著,以及对部分层的训练准确率略低于整体模型训练的发现,本文按照Transformer层对模型进行分割,同时引入低秩适应(Low⁃rank adaption,LoRA)进一步降低资源开销和提升安全性。因此,在设备端,仅对最后几层进行低秩适应和训练,然后上传至服务器进行聚合。为了降低开销并保证模型性能,本文提出了基于联邦分割学习与LoRA的RoBERTa预训练模型微调方法。通过联合优化边缘设备的计算频率和模型微调的秩,在资源受限的情况下最大化秩,提高模型的准确率。仿真结果显示,仅训练LLMs最后3层的情况下,在一定范围内(1~32)增加秩的取值可以提高模型的准确率。同时,增大模型每轮的容忍时延和设备的能量阈值可以进一步提升模型的准确率。

AI大语言模型对高职智能学习场景的价值研究

相比于AI大语言模型在资料查询、论文写作、语言学习等教育领域的应用,其对学生技能学习的影响更值得重视。以ChatGPT为代表的大语言模型对高职学生的技能培养和高职教育教学改革提出了新要求。总结已有研究对人工智能技术迭代背景下人机关系的认识,分析基于人机共创的高职智能学习场景,探寻AI语言大模型对高职智能学习场景的价值。

软土深基坑闪撑速降技术及工程应用研究

深基坑无支撑暴露期间变形显著,为缩短基坑暴露时间,控制基坑变形发展,文章研发了闪撑速降技术,其借鉴伺服轴力补偿钢支撑系统及滑模技术,能对基坑进行提前主动支护,从而大幅缩短了暴露时间。结合杨高北路站深基坑开展工程应用,针对现场监测数据进行对比分析,快速支撑区的最大侧移平均值为58.5 mm,相较普通支撑区域减少了约7 mm,验证了新工法对于基坑围护结构变形控制的有效性,可为类似工程提供参考。

FL-DLight3-4000激光器激光熔覆过程温度场数值模拟分析

激光熔覆技术有着良好的发展前景。文中利用ANSYS中的生死单元法模拟了基体在激光熔覆过程中的温度场,利用APDL语言编写加载移动矩形热源。通过对温度场的模拟,得出了不同的激光功率及扫描速度对温度场的影响,即功率越大,其基体(45钢)同时间内升高到相同温度所需时间越少;扫描速度越快,基体同时间内升温越慢。文中初步探究了激光熔覆中存在的端部效应问题,结果表明,采用预留方案能有效地解决端部效应问题。最后,基于机器人控制的FL-DLight3-4000激光器进行了试验,采用旁轴送粉喷嘴进行送粉,设置激光功率为3 kW,扫描速度为0.005 m·s^(-1),送粉率25 g·min^(-1)。文中测量了距离第一道熔覆层中心25 mm处的不同时刻的温度,并与ANSYS中的模拟结果进行比较,证明试验结果与模拟结果基本一致。

外语损耗研究的动向及建议

最近20年,国内外学术界逐渐给予外语损耗研究较多关注。本文系统梳理了14项国际较有影响力的研究,结果发现,在词汇和形态层面,形态最先被损耗,词汇最易受损耗,而语音、句法、语义、语用方面的研究较少;损耗速度整体呈现“前快—中稳—后快”的趋势,但对是否存在起始平台还存在争议;就影响损耗的语外因素而言,语言停用并非导致外语损耗的唯一因素,起始水平对外语损耗具有重要预测作用,不过还需要对语外因素的交互关系进一步研究。本文就如何确定外语损耗研究的基准线、选择合适的研究方法、设计研究的整体框架等方面阐释了对国内研究的建议。

The Role of Lexis in Developing EFL Learners＇ Speaking Skill

EFL （English as a Foreign Language） speaking is a very demanding skill that requires learners＇ socio-pragmatic as well as strategic competence in any interactional situation, and lexis proves to play a crucial role in this process. However, few studies have investigated how both EFL teachers and learners view and analyze situations in which learners are not producing enough spoken language in class, and the reasons behind them. The present study will pinpoint the significant role of lexis in Moroccan learners＇ speaking production. To this end, 40 EFL teachers and 200 Moroccan high school students are surveyed and interviewed to reveal their perceptions of the speaking skill and the corresponding high significance of lexis in this instance. Results show that both teachers and learners identify vocabulary deficiency as the main factor behind students＇ inability to speak English. In the present paper, among the many suggestions that could be proposed to deal with this situation, it is argued that one efficient way would be to assist the students during the process of L2 （second language） vocabulary learning through vocabulary learning strategy instruction. Pedagogical and research implication will be given in response to the difficulties encountered in this area as have been identified by the EFL teachers and learners surveyed.