Reinforcement prompting for financial synthetic data generation

The deployment of Large Language Models (LLMs) in the financial field is one of the most forward-looking and quickly changing AI research areas. This paper presents a thorough scientometric study of the top 200 most cited articles, which deal with the conjunction of LLMs, finance, and stock markets, and are indexed in Scopus. The analysis carries out a detailed examination of the publication patterns, influential authors and institutions, main research areas, methods used, and the intellectual framework of this new field. The disclose of the research output has been increased enormously; the main form of it is conference papers, and it is all through collaboration between the global academic and industrial research centers. Financial sentiment analysis and market prediction, the building and evaluating of domain-specific financial LLMs (FinLLMs), and lastly the application of the mentioned FinLLMs in areas such as financial analytics and decision-making support are examples of research that has been clustered together. Nevertheless, a big part of the research is directed towards trust, ethics, and risks in the financial domain. Moreover, the study points out that practical applications, such as algorithmic trading, risk management, and compliance with regulations, are among the most highlighted in the fields of LLMs in finance. This scientometric review offers a primary map of the vast conceptual territory marked by high-impact research, which means it can be very helpful to researchers, practitioners, and policymakers in getting a better understanding of the existing situation and thus, pointing out future research directions in the area of LLMs in finance.

Named entity recognition (NER) plays a vital role in extracting critical medical entities from health care records, facilitating applications such as clinical decision support and data mining. Developing robust NER models for low-resource languages, such as Estonian, remains a challenge due to the scarcity of annotated data and domain-specific pretrained models. Large language models (LLMs) have proven to be promising in understanding text from any language or domain. This study addresses the development of medical NER models for low-resource languages, specifically Estonian. We propose a novel approach by generating synthetic health care data and using LLMs to annotate them. These synthetic data are then used to train a high-performing NER model, which is applied to real-world medical texts, preserving patient data privacy. Our approach to overcoming the shortage of annotated Estonian health care texts involves a three-step pipeline: (1) synthetic health care data are generated using a locally trained GPT-2 model on Estonian medical records, (2) the synthetic data are annotated with LLMs, specifically GPT-3.5-Turbo and GPT-4, and (3) the annotated synthetic data are then used to fine-tune an NER model, which is later tested on real-world medical data. This paper compares the performance of different prompts; assesses the impact of GPT-3.5-Turbo, GPT-4, and a local LLM; and explores the relationship between the amount of annotated synthetic data and model performance. The proposed methodology demonstrates significant potential in extracting named entities from real-world medical texts. Our top-performing setup achieved an F1-score of 0.69 for drug extraction and 0.38 for procedure extraction. These results indicate a strong performance in recognizing certain entity types while highlighting the complexity of extracting procedures. This paper demonstrates a successful approach to leveraging LLMs for training NER models using synthetic data, effectively preserving patient privacy. By avoiding reliance on human-annotated data, our method shows promise in developing models for low-resource languages, such as Estonian. Future work will focus on refining the synthetic data generation and expanding the method's applicability to other domains and languages.

In today’s rapidly evolving business landscape, Artificial Intelligence (AI), and specifically Large Language Models (LLMs), are redefining how organizations operate, make decisions, and engage with customers. AI-driven technologies have become indispensable, providing businesses with powerful tools to streamline operations, derive actionable insights from vast data, and foster more meaningful customer interactions. For business leaders, scholars, and practitioners alike, understanding the transformative potential of AI isn’t just advantageous—it’s essential to staying competitive in an increasingly data-driven world.This editorial delves into recent scholarly advancements in LLM applications within business contexts, analyzing studies that explore AI’s potential across various domains, from decision support to creative industries. By introducing a structured framework, this editorial highlights key insights and contributions from recent studies, assessing their value to academia and industry. The following comparative analysis sheds light on how these innovations shape our understanding of AI’s role in business while pointing to future research directions.Puyt and Madsen's (2024) study stands out as a foundational exploration of LLM accuracy, assessing ChatGPT-4's ability to recount the history of the SWOT analysis-a vital business strategy tool. Their findings reveal that, while ChatGPT-4 effectively conveys general concepts, it struggles with detailed historical information, often producing inaccuracies or "hallucinations." This gap underscores the need for LLMs to be trained with verified academic data, particularly for strategic business applications that demand precision. This study not only contributes to the literature by proposing methods to evaluate AI accuracy in historical contexts but also highlights the importance of rigorous information vetting in industry settings where reliability is crucial.In contrast, Raikov et al. (2024) explore a hybrid intelligence model that combines LLM capabilities with explainable AI (XAI) principles to enhance human-machine collaboration. Their approach emphasizes cognitive semantics, improving transparency and decision-making efficiency. The hybrid model's real-time adaptability addresses the needs of complex, regulated industries such as finance and healthcare, where trust in AI decisions is paramount. Academically, this study provides a valuable addition to XAI literature by demonstrating how LLMs can bridge the gap between AI autonomy and human oversight, making it a model for future human-AI interactions in complex business environments.Another significant study by Mariotti and colleagues (2024) examines the integration of LLMs with enterprise knowledge graphs to enhance data-driven decision-making. By enabling organizations to leverage knowledge graphs for more accurate and scalable data retrieval, this research provides a robust framework for businesses seeking efficient knowledge management systems. The academic contribution here lies in advancing the dialogue between LLMs and knowledge graphs, emphasizing ethical data handling and quality standards essential for industry applications. For enterprises, the study offers practical solutions to achieve streamlined data management, balancing automation with privacy and security. 2024) take a different approach, investigating LLMs' role in creative industries, specifically within fashion design. They introduce a hybrid intelligence model that supports creative processes, allowing AI to complement rather than replace human ingenuity. While LLMs in this field demonstrate potential in automating repetitive design tasks and enhancing customer personalization, the study reveals limitations in AI's ability to handle spatial and stylistic nuances. This study's academic contribution lies in promoting human-AI co-creation, inspiring further research into AI applications across diverse creative sectors, including media and marketing.Collectively, these studies not only illuminate LLMs' transformative potential in business but also highlight critical ethical and operational considerations. Ensuring accuracy, transparency, and data privacy are vital to responsibly integrating AI into business workflows. Future research should focus on enhancing LLM accuracy, refining hybrid intelligence models, and exploring creative AI applications, all while maintaining ethical standards. As LLMs evolve, interdisciplinary collaborations will be essential to harness their full potential, making AI an ethical, effective, and innovative force in the business world.

Self-bootstrapping automated program repair: using LLMs to generate and evaluate synthetic training data for bug repair

AI technology’s rapid advancements have led to its widespread adoption in diverse industries, such as support agents, healthcare, education, software development and finance because such AI technologies has the ability to generated human-like text to solve a complex user’s problem. Nevertheless, the effectiveness and success of AI models greatly depends on large language model that contain extensive, varied, and top-notch datasets and social network platforms for both training and evaluation purposes that is sourced from web and directly form user interaction that will also contain sensitive information. However, this power capabilities and progress have raised significant concerns related data security and privacy, particularly regarding protection of user data including social network during training and deployment phase. Large language models process and generate huge amount of data which have the potential to unintentionally memorise and repeat personal sensitive information like location data, social security number, and phone numbers which are shared by users that can lead to pose a risk of data leaking and degrade the accuracy of model training and information. This paper will perform a comprehensive and systematic review to examine the fundamentals principle of large language model with particularly concentration on data privacy concern associated with large language model during training data and potential biases. Furthermore, we analysis and evaluated the current level of vulnerabilities, privacy challenges, and examine new security and privacy attacks targeting LLMs that cause of misuse and information leakage. Additionally, we proposed a set of best practices aimed to protect user data. Recognising the significance of conducting such research on protecting privacy and improving security is crucial because it ensures the continuous progress and public trust in LLM technology.

Mathematical Problem Solving in Arabic: Assessing Large Language Models

Large language models (LLMs) have made significant advances in the field of natural language processing (NLP). However, their centralized training approach faces challenges related to data privacy, communication efficiency, and robustness against adversarial attacks, particularly in wireless environments. With the gradual depletion of high-quality public data, there is an urgent need to leverage private data distributed across various parties. Although federated learning (FL) offers a privacy-preserving collaborative training paradigm, it struggles to meet the high computational demands of edge devices and remains vulnerable to adversarial attacks. This paper introduces <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ROFED</small>-LLM, a novel framework for robust, privacy-preserving training of LLMs on decentralized private data over wireless networks. By integrating split federated learning, which partitions the model across devices to enhance privacy, with adaptive jamming defense mechanisms, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ROFED</small>-LLM enables collaborative LLM training without raw data sharing while ensuring resilience against wireless adversarial attacks. Our multi-modal defense strategy combines model-level protections, such as differential privacy and dynamic pruning, with communication-level safeguards, including adaptive beamforming which optimizes wireless signal transmission to mitigate interference, and resource allocation optimization. Extensive experiments across diverse NLP tasks demonstrate <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ROFED</small>-LLM's superiority, achieving a 12.87% improvement in privacy preservation and 18.26% enhancement in jamming resilience compared to existing methods such as FedAvg and SCAFFOLD, with only a marginal 3.94% trade-off in model accuracy. Our code repository has been open sourced at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://anonymous.4open.science/r/RoFed-LLM-54E1</uri>.

Legal question answering (LQA) relies on supervised methods to automatically handle law-related queries. These solutions require a substantial amount of carefully annotated data for training, which makes the process very costly. Although large language models (LLMs) show promise in zero-shot QA, their computational demands limit their practical use, making specialized small language models (SLMs) more favorable. Furthermore, the growing interest in synthetic data generation has recently surged, spurred by the impressive generation capabilities of LLMs. This paper presents Ace-Attorney, an LLM distillation approach devised to develop LQA data and supervised models without human annotation. Given a textual prompt, a frozen LLM generates artificial examples that are used as knowledge to train a student SLM with an order of magnitude fewer parameters. Taking into account a realistic retrieval-based scenario to fetch the correct document for answer generation, we propose Selective Generative Paradigm, a novel approach designed to improve retrieval efficacy. Extensive experiments demonstrate the effectiveness and efficiency of distilled models on Syn-LeQA, our human-free synthetic dataset, and a public expert-annotated corpus. Notably, by using only a few dozen training samples, our best SLM achieves LLM-comparable performance with $$\approx $$ ≈ 1200% less CO $$_2$$ 2 emissions. The data and the code to fully reproduce our results are available at https://github.com/disi-unibo-nlp/ace-attorney.

Purpose With accelerating advances in artificial intelligence (AI) and digital transformation, the construction domain is rapidly adopting large language models (LLMs) to improve efficiency, safety and sustainability. However, a comprehensive understanding of their applications, adaptation strategies and evaluation practices in this domain remains limited. This paper provides a systematic landscape of LLM research and applications in construction, clarifying their roles, integration pathways and associated challenges. Design/methodology/approach A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline, combining bibliometric and content analysis of publications from 2020 to 2025. The review examines the evolution, implementation strategies and performance characteristics of LLMs in construction domains. Findings LLMs have been increasingly implemented across diverse construction tasks. Most studies utilize pretrained models, adapted through fine-tuning, retrieval-augmented generation or prompt engineering. While LLMs demonstrate strong reasoning and semantic understanding capabilities, challenges persist in domain adaptation, hallucination control, data privacy and lifecycle integration. Practical implications The results provide insights for researchers and practitioners on how LLMs can be effectively implemented throughout the construction domain. The paper highlights the need for developing domain-specific evaluation standards, establishing governance frameworks and aligning LLM applications with existing digital ecosystems such as building information modeling and digital twins. Originality/value This paper presents a comprehensive systematic review focused on LLM applications in construction. By mapping the technological landscape and summarizing key implementation strategies, it establishes a theoretical and practical foundation for advancing intelligent, data-driven and human-centered construction in the era of generative AI.

Large Language Models (LLMs) became wide-spread during only the last couple of years and are used in almost every scientific and non-scientific domain. Understanding opportunities, applications and limitations of LLMs is crucial for a risk-free, effective and useful implementation of LLMs into scientific workflows. We demonstrate that their effectiveness is maximized not through autonomous operation but through careful integration with specialized tools and contextual knowledge bases.&amp;#160;Using local deployments of modern LLMs (QWen-2.5-coder, LLaMA, Mistral) comes with a number of benefits in a scientific context. Our approach employs vector embeddings for enhanced context retention and metadata databases for structured data access, enabling guided, context-aware interactions with the LLM. Local deployments allow for improved data handling and privacy, improved cost management and a higher degree of customization. Energy consumption can more easily be observed and managed, which can be a crucial property of such a system, especially compared to the newest generation of LLMs, which have extensive power&amp;#160; (and cost) requirements.Opportunities and limitations are explored through two case studies: (1) an LLM-driven system that queries metadata databases to retrieve data from common open data sources and harmonizes patio-temporal subsets into data-cubes, and (2) a VBA-to-Python code translation project to preserve a legacy selection-system forest management software, which was developed in ACCESS / VBA over more than two decades. The LLM's translation process and reasoning are preserved in a vector database for consistent context maintenance and the original as well as the &amp;#8218;new&amp;#8216; code is searchable using the LLM to aid rebuilding a modernized software.&amp;#160;Results suggest that this tool-augmented approach leads to a more reliable and maintainable solution compared to purely LLM-driven implementations, suggesting a new paradigm for integrating AI in scientific workflows where LLMs rather facilitate than replace domain-specific tools and human expertise.

Background: Accurate identification of cancer progression events from electronic health records (EHRs) can help enable promising oncology applications such as predicting disease trajectory, assessing treatment efficacy, and generating real-world evidence. These use cases require both large-scale and high-quality data but manual abstraction of real-world progression (rwP) is time-intensive, difficult to scale, and inherently challenging given the varied and unstructured ways it can be documented across cancer types. Large language models (LLMs) offer a scalable alternative, but their accuracy relative to expert human abstractors is unclear. We evaluated the ability of LLMs to extract rwP events and dates across 7 cancer types and assessed how using LLM-extracted data impacted real-world progression-free survival (rwPFS) estimates compared to using human-abstracted data. Methods: We applied LLM-based extraction techniques to unstructured EHR text for 7 cancer types from the Flatiron Health Research Database: bladder (N=377), breast (N=1000), colorectal (N=564), hepatocellular (N=217), renal cell (N= 229), non–small cell lung (N=1000), and small cell lung (N=955). Prompt engineering strategies including zero-shot, few-shot, and chain-of-thought were tested to optimize performance. We measured agreement between the LLM and abstractor on the presence of rwP (Y/N) and first rwP date (± 30 days) in the first-line (1L) setting. To contextualize the LLM’s ability to extract rwP relative to an expert human abstractor, we evaluated the difference in F1 scores between both curation approaches using a duplicate human-abstracted reference dataset. We also compared rwPFS calculated from LLM-curated data versus human abstractor-curated data for 1000 patients in each cancer type, indexed to 1L start date. Results: Across all cancer types, agreement between the LLM and abstractor on the presence of at least 1 rwP event ranged from 86%-90% while first rwP date agreement ranged from 80%-92%. The difference in F1 score between the LLM and human abstraction was within 3-8 points across cancer types. A comparison of rwPFS between the LLM and human abstractors showed &amp;lt;1 month difference in median rwPFS and overlapping 95% confidence intervals across all cancer types. Discussion: LLMs extracted rwP with high performance, achieving F1 scores similar to expert human abstraction. Across 7 distinct cancers, agreement with human-abstracted data aligned with published inter-abstractor reliability benchmarks, and rwPFS estimates were nearly identical across curation approaches, demonstrating both the generalizability and validity of the approach. These results highlight the potential of LLMs to extract high-quality clinical endpoints at scale, helping to advance research, enhance applications such as predictive algorithms, and ultimately supporting more personalized and effective cancer care. Citation Format: Aaron B. Cohen, Konstantin Krismer, Kelly Magee, James Gippetti, Aaron Dolor, Tori Williams, Erin Fidyk, Hank Kim, Qianyu Yuan, Melissa Estevez. Using large language models for scalable extraction of real-world progression events across multiple cancer types [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B006.

Low self-esteem (LoST) is a latent yet critical psychosocial risk factor that predisposes individuals to depressive disorders. Although structured tools exist to assess self-esteem, their limited clinical adoption suggests that relevant indicators of LoST remain buried within unstructured clinical narratives. The scarcity of annotated clinical notes impedes the development of natural language processing (NLP) models for its detection. Manual chart reviews are labor-intensive and large language model (LLM)-driven (weak) labeling raises privacy concerns. Past studies demonstrate that NLP models trained on LLM-generated synthetic clinical notes achieve performance comparable to, and sometimes better than those trained on real notes. This highlights synthetic data’s utility for augmenting scarce clinical corpora while reducing privacy concerns. Prior efforts have leveraged social media data, such as Reddit, to identify linguistic markers of low self-esteem; however, the linguistic and contextual divergence between social media and clinical text limits the generalizability of these models. To address this gap, we present a novel framework that generates context-enhanced synthetic clinical notes from social media narratives and evaluates the utility of small language models for identifying expressions of low self-esteem. Our approach includes a mixed-method evaluation framework: (i) structure analysis, (ii) readability analysis, (iii) linguistic diversity, and (iv) contextual fidelity of LoST cues in source Reddit posts and synthetic notes. This work offers a scalable, privacy-preserving solution for synthetic data generation for early detection of psychosocial risks such as LoST and demonstrates a pathway for translating mental health signals in clinical notes into clinically actionable insights, thereby identifying patients at risk.

This work focuses on refining a decentralized large language model (LLM) tailored for finetuning on privacy-sensitive university data. Devolved AI models, designed to operate across multiple distributed nodes, offer a promising solution for handling sensitive information by ensuring data remains localized at its source while collaboratively training a global model. The key challenge addressed in this study is the adaptation and fine-tuning of a decentralized LLM to work effectively with heterogeneous, privacyrestricted datasets typical in university environments, such as student records, research data, and administrative information. Our approach involves enhancing the LLM’s ability to handle domain-specific language through targeted fine-tuning on anonymized university datasets. The model is further optimized for efficient decentralized learning, ensuring data privacy while improving model performance. Advanced techniques, such as differential privacy and secure aggregation, are incorporated to strengthen data protection during finetuning. A notable innovation of our work is the development of a comprehensive Devolved AI product that not only manages decentralized finetuning but also incorporates an LLM as a judge to score model improvements. This product automates the end-to-end process—from data ingestion and model fine-tuning to evaluation—by leveraging the LLM to provide objective, detailed feedback on model performance. Initial results demonstrate that the refined LLM achieves high accuracy in downstream tasks, including automated document summarization, query answering, and policy generation, without compromising data privacy. This research highlights the potential of decentralized AI systems in privacy-sensitive domains and paves the way for scalable, secure AI solutions in academic institutions. Future work will focus on expanding the model’s applicability to broader educational datasets and further optimizing the finetuning frameworks and evaluation methods employed by the Devolved AI product.

The aim of this study is to assess the role of psycholinguistics and large language models (LLMs), such as GPT-3, in profiling, specifically their ability to expand the possibilities for behavioral analysis based on speech patterns. Psycholinguistics is an effective tool for creating psychological profiles of individuals through language analysis. The integration of LLMs into this process significantly enhances the accuracy and speed of analyzing large volumes of text, uncovering emotional, cognitive, and social behavioral markers. The research methods employed include a mixed approach combining both qualitative and quantitative speech analysis. The data sample consisted of 200 text fragments sourced from social media, emails, and interviews. For analysis, both LLMs (GPT-3) and traditional psycholinguistic methods (LIWC) were used. GPT-3 was applied for the automated analysis of speech patterns, and the results were compared with those obtained through LIWC. Statistical testing was also conducted to examine the differences in marker frequencies. The results indicated that GPT-3 identified a higher frequency of emotional, cognitive, and social markers compared to LIWC, although these differences were not statistically significant (p &gt; 0.05). LLMs demonstrate a significant advantage in processing speed, making them an effective tool for analyzing large amounts of data. Additionally, GPT-3 exhibited a higher level of accuracy in identifying emotional and cognitive markers, particularly in situations where traditional methods may not provide precise results due to limitations in contextual analysis. The key findings of the study suggest that GPT-3 is a powerful tool for psycholinguistic analysis and profiling, capable of enhancing the accuracy of psychological profile creation in fields such as criminal investigations and human resource management. However, ethical considerations regarding data privacy and potential limitations in the accuracy of LLMs must be taken into account. Future research should focus on integrating LLMs with other analytical methods to develop more effective and ethical profiling solutions.

A survey for large language models in biomedicine.