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.

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.

Large animal models for Huntington's disease research

Huntington'sdisease(HD)isahereditary neurodegenerative disorder for which there is currently no effectivetreatmentavailable.Consequently,the development of appropriate disease models is critical to thoroughly investigate disease progression.The genetic basis of HD involves the abnormal expansion of CAG repeats in the huntingtin(HTT)gene,leading to the expansion of a polyglutamine repeat in the HTT protein.Mutant HTT carrying the expanded polyglutamine repeat undergoes misfolding and forms aggregates in the brain,which precipitate selective neuronal loss in specific brain regions.Animal models play an important role in elucidating the pathogenesis of neurodegenerative disorders such as HD and in identifying potential therapeutic targets.Due to the marked species differences between rodents and larger animals,substantial efforts have been directed toward establishing large animal models for HD research.These models are pivotal for advancing the discovery of novel therapeutic targets,enhancing effective drug delivery methods,and improving treatment outcomes.We have explored the advantages of utilizing large animal models,particularly pigs,in previous reviews.Since then,however,significant progress has been made in developing more sophisticated animal models that faithfully replicate the typical pathology of HD.In the current review,we provide a comprehensive overview of large animal models of HD,incorporating recent findings regarding the establishment of HD knock-in(KI)pigs and their genetic therapy.We also explore the utilization of large animal models in HD research,with a focus on sheep,non-human primates(NHPs),and pigs.Our objective is to provide valuable insights into the application of these large animal models for the investigation and treatment of neurodegenerative disorders.

Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

Large language models in laparoscopic surgery: A transformative opportunity

This opinion paper explores the transformative potential of large language models(LLMs)in laparoscopic surgery and argues for their integration to enhance surgical education,decision support,reporting,and patient care.LLMs can revolutionize surgical education by providing personalized learning experiences and accelerating skill acquisition.Intelligent decision support systems powered by LLMs can assist surgeons in making complex decisions,optimizing surgical workflows,and improving patient outcomes.Moreover,LLMs can automate surgical reporting and generate personalized patient education materials,streamlining documentation and improving patient engagement.However,challenges such as data scarcity,surgical semantic capture,real-time inference,and integration with existing systems need to be addressed for successful LLM integration.The future of laparoscopic surgery lies in the seamless integration of LLMs,enabling autonomous robotic surgery,predictive surgical planning,intraoperative decision support,virtual surgical assistants,and continuous learning.By harnessing the power of LLMs,laparoscopic surgery can be transformed,empowering surgeons and ultimately benefiting patients.

Evaluating the role of large language models in inflammatory bowel disease patient information

This letter evaluates the article by Gravina et al on ChatGPT’s potential in providing medical information for inflammatory bowel disease patients.While promising,it highlights the need for advanced techniques like reasoning+action and retrieval-augmented generation to improve accuracy and reliability.Emphasizing that simple question and answer testing is insufficient,it calls for more nuanced evaluation methods to truly gauge large language models’capabilities in clinical applications.

LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.

Large multimodal models assist in psychiatry disorders prevention and diagnosis of students

Students are considered one of the groups most affected by psychological pro-blems.Given the highly dangerous nature of mental illnesses and the increasing-ly serious state of global mental health,it is imperative for us to explore new me-thods and approaches concerning the prevention and treatment of mental illne-sses.Large multimodal models(LMMs),as the most advanced artificial intelligen-ce models(i.e.ChatGPT-4),have brought new hope to the accurate prevention,diagnosis,and treatment of psychiatric disorders.The assistance of these models in the promotion of mental health is critical,as the latter necessitates a strong foundation of medical knowledge and professional skills,emotional support,stigma mitigation,the encouragement of more honest patient self-disclosure,reduced health care costs,improved medical efficiency,and greater mental health service coverage.However,these models must address challenges related to health,safety,hallucinations,and ethics simultaneously.In the future,we should address these challenges by developing relevant usage manuals,accountability rules,and legal regulations;implementing a human-centered approach;and intelligently upgrading LMMs through the deep optimization of such models,their algorithms,and other means.This effort will thus substantially contribute not only to the maintenance of students’health but also to the achievement of global sustainable development goals.

Potential use of large language models for mitigating students’problematic social media use:ChatGPT as an example

The problematic use of social media has numerous negative impacts on individuals'daily lives,interpersonal relationships,physical and mental health,and more.Currently,there are few methods and tools to alleviate problematic social media,and their potential is yet to be fully realized.Emerging large language models(LLMs)are becoming increasingly popular for providing information and assistance to people and are being applied in many aspects of life.In mitigating problematic social media use,LLMs such as ChatGPT can play a positive role by serving as conversational partners and outlets for users,providing personalized information and resources,monitoring and intervening in problematic social media use,and more.In this process,we should recognize both the enormous potential and endless possibilities of LLMs such as ChatGPT,leveraging their advantages to better address problematic social media use,while also acknowledging the limitations and potential pitfalls of ChatGPT technology,such as errors,limitations in issue resolution,privacy and security concerns,and potential overreliance.When we leverage the advantages of LLMs to address issues in social media usage,we must adopt a cautious and ethical approach,being vigilant of the potential adverse effects that LLMs may have in addressing problematic social media use to better harness technology to serve individuals and society.

Evaluating Privacy Leakage and Memorization Attacks on Large Language Models (LLMs) in Generative AI Applications

The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. We describe different black-box attacks from potential adversaries and study their impact on the amount and type of information that may be recovered from commonly used and deployed LLMs. Our research investigates the relationship between PII leakage, memorization, and factors such as model size, architecture, and the nature of attacks employed. The study utilizes two broad categories of attacks: PII leakage-focused attacks (auto-completion and extraction attacks) and memorization-focused attacks (various membership inference attacks). The findings from these investigations are quantified using an array of evaluative metrics, providing a detailed understanding of LLM vulnerabilities and the effectiveness of different attacks.

Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework

Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks.

Implementation of Digital Classroom State System for Teachers and Students Based on Large Models

Deep learning has become a hot field of artificial intelligence,and the deep learning large model framework has become a bridgehead for the active layout of Chinese and foreign technology companies.Large models play a significant role in the application field,greatly improving the efficiency of training and optimization,and contributing to the landing of many innovative artificial intelligence tools.Based on the Chinese PaddlePaddle large model framework,an application system is designed in combination with the intelligent classroom teaching scenario,which uses machine vision algorithms to distinguish and present teachers’and students’behaviors,that is,the digitization and multi-classification scheme of class character states.After having digital data,data analysis can be carried out to evaluate the class status of teachers and students,and the traditional subjective judgment such as peacetime grades and teaching ability can be upgraded to the objective judgment of artificial intelligence.

Large animal models of cardiac ischemia-reperfusion injury:Where are we now?

Large animal models of cardiac ischemia-reperfusion are critical for evaluation of the efficacy of cardioprotective interventions prior to clinical translation.Nonetheless,current cardioprotective strategies/interventions formulated in preclinical cardiovascular research are often limited to small animal models,which are not transferable or reproducible in large animal models due to different factors such as:(i)complex and varied features of human ischemic cardiac disease(ICD),which are challenging to mimic in animal models,(ii)significant differences in surgical techniques applied,and(iii)differences in cardiovascular anatomy and physiology between small versus large animals.This article highlights the advantages and disadvantages of different large animal models of preclinical cardiac ischemic reperfusion injury(IRI),as well as the different methods used to induce and assess IRI,and the obstacles faced in using large animals for translational research in the settings of cardiac IR.

DeBERTa-GRU: Sentiment Analysis for Large Language Model

Modern technological advancements have made social media an essential component of daily life.Social media allow individuals to share thoughts,emotions,and ideas.Sentiment analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the text.Sentiment analysis is essential in business and society because it impacts strategic decision-making.Sentiment analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance correlations.The execution time increases due to the sequential processing of the sequence models.However,the calculation times for the Transformer models are reduced because of the parallel processing.This study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their limitations.In particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment analysis.Using the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics correctly.The proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

A large language model-powered literature review for high-angle annular dark field imaging

High-angle annular dark field(HAADF)imaging in scanning transmission electron microscopy(STEM)has become an indispensable tool in materials science due to its ability to offer sub-°A resolution and provide chemical information through Z-contrast.This study leverages large language models(LLMs)to conduct a comprehensive bibliometric analysis of a large amount of HAADF-related literature(more than 41000 papers).By using LLMs,specifically ChatGPT,we were able to extract detailed information on applications,sample preparation methods,instruments used,and study conclusions.The findings highlight the capability of LLMs to provide a new perspective into HAADF imaging,underscoring its increasingly important role in materials science.Moreover,the rich information extracted from these publications can be harnessed to develop AI models that enhance the automation and intelligence of electron microscopes.

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

Large Language Model Based Semantic Parsing for Intelligent Database Query Engine

With the rapid development of artificial intelligence, large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. These models have great potential to enhance database query systems, enabling more intuitive and semantic query mechanisms. Our model leverages LLM’s deep learning architecture to interpret and process natural language queries and translate them into accurate database queries. The system integrates an LLM-powered semantic parser that translates user input into structured queries that can be understood by the database management system. First, the user query is pre-processed, the text is normalized, and the ambiguity is removed. This is followed by semantic parsing, where the LLM interprets the pre-processed text and identifies key entities and relationships. This is followed by query generation, which converts the parsed information into a structured query format and tailors it to the target database schema. Finally, there is query execution and feedback, where the resulting query is executed on the database and the results are returned to the user. The system also provides feedback mechanisms to improve and optimize future query interpretations. By using advanced LLMs for model implementation and fine-tuning on diverse datasets, the experimental results show that the proposed method significantly improves the accuracy and usability of database queries, making data retrieval easy for users without specialized knowledge.

Large animal ischemic stroke models: replicating human stroke pathophysiology

The high morbidity and mortality rate of ischemic stroke in humans has led to the development of numerous animal models that replicate human stroke to further understand the underlying pathophysiology and to explore potential therapeutic interventions.Although promising therapeutics have been identified using these animal models,with most undergoing significant testing in rodent models,the vast majority of these interventions have failed in human clinical trials.This failure of preclinical translation highlights the critical need for better therapeutic assessment in more clinically relevant ischemic stroke animal models.Large animal models such as non-human primates,sheep,pigs,and dogs are likely more predictive of human responses and outcomes due to brain anatomy and physiology that are more similar to humans-potentially making large animal testing a key step in the stroke therapy translational pipeline.The objective of this review is to highlight key characteristics that potentially make these gyrencephalic,large animal ischemic stroke models more predictive by comparing pathophysiological responses,tissue-level changes,and model limitations.

May ChatGPT be a tool producing medical information for common inflammatory bowel disease patients’questions?An evidencecontrolled analysis

Artificial intelligence is increasingly entering everyday healthcare.Large language model(LLM)systems such as Chat Generative Pre-trained Transformer(ChatGPT)have become potentially accessible to everyone,including patients with inflammatory bowel diseases(IBD).However,significant ethical issues and pitfalls exist in innovative LLM tools.The hype generated by such systems may lead to unweighted patient trust in these systems.Therefore,it is necessary to understand whether LLMs(trendy ones,such as ChatGPT)can produce plausible medical information(MI)for patients.This review examined ChatGPT’s potential to provide MI regarding questions commonly addressed by patients with IBD to their gastroenterologists.From the review of the outputs provided by ChatGPT,this tool showed some attractive potential while having significant limitations in updating and detailing information and providing inaccurate information in some cases.Further studies and refinement of the ChatGPT,possibly aligning the outputs with the leading medical evidence provided by reliable databases,are needed.

Current opinions on large cellular models

1|INTRODUCTION.Large language models(LLMs)have made breakthroughs in natural language processing(NLP)and understanding,and have brought revolutions in many other fields[1-4].Inspired by those successes,several large cellular models(LCMs)adopting similar structures of LLMs have been developed for single-cell transcriptomics,including(but not limited to)scBERT[5],Geneformer[6],scGPT[7],scFoundation[8],and GeneCompass[9].The practices of these models have shown LCMs’power and potential in various biological tasks and illustrated the possibilities of revolutionizing future biological studies by LCMs.