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Journal articles on the topic 'Gemma Model'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

C. Grassmann, Ana Clara, Juliana C. Feitosa, José Remo F. Brega, and Kelton A. P. da Costa. "Evaluation of Transformer-Based Large Language Models for Email Spam Detection Using BERT, Phi, and Gemma." Statistics, Optimization & Information Computing 13, no. 2 (2024): 459–73. https://doi.org/10.19139/soic-2310-5070-2267.

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In this paper, we study how LLMs based on the transformer architecture work and the possibility of adjusting these models to use only the body of email messages to classify them as spam or ham. The models studied are BERT, Gemma, and Phi. All of them underwent quantization stages, fine-tuning with a real dataset, and evaluation with metrics commonly used in binary classification problems. The Gemma model achieves over 99% accuracy in detecting spam, standing out as the best among the compared models.

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Goldenberg, Gemma. "Being curious." Nursery World 2023, no. 2 (2023): 22–23. http://dx.doi.org/10.12968/nuwa.2023.2.22.

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Colombo, Marco, Andrea De Santis, Bruce C. Hanson, and Michael Fairweather. "Prediction of Horizontal Gas–Liquid Segregated Flow Regimes with an All Flow Regime Multifluid Model." Processes 10, no. 5 (2022): 920. http://dx.doi.org/10.3390/pr10050920.

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The generalized multifluid modelling approach (GEMMA) has been developed to handle the multiplicity of flow regimes and the coexistence of interfaces of largely different scales in multiphase flows. The solver, based on the OpenFOAM reactingEulerFoam family of solvers, adds interface resolving-like capabilities to the multifluid solver in the cells occupied by large interfaces. In this paper, GEMMA is further developed to predict stratified and slug flow regimes in horizontal ducts. The suppression of the turbulence and the wall-like behaviour of large interfaces is modelled with an additional

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Reguzzoni, M., and D. Sampietro. "GEMMA: An Earth crustal model based on GOCE satellite data." International Journal of Applied Earth Observation and Geoinformation 35 (March 2015): 31–43. http://dx.doi.org/10.1016/j.jag.2014.04.002.

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BADUROWICZ, Marcin, Stanisław SKULIMOWSKI, and Maciej LASKOWSKI. "FEASIBILITY OF USING LOW-PARAMETER LOCAL LLMS IN ANSWERING QUESTIONS FROM ENTERPRISE KNOWLEDGE BASE." Applied Computer Science 20, no. 4 (2024): 175–91. https://doi.org/10.35784/acs-2024-46.

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This paper evaluates the feasibility of deploying locally-run Large Language Models (LLMs) for retrieval-augmented question answering (RAG-QA) over internal knowledge bases in small and medium enterprises (SMEs), with a focus on Polish-language datasets. The study benchmarks eight popular open-source and source-available LLMs, including Google’s Gemma-9B and Speakleash’s Bielik-11B, assessing their performance across closed, open, and detailed question types, with metrics for language quality, factual accuracy, response stability, and processing efficiency. The results highlight that desktop-c

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Zouidine, Mohamed, and Mohammed Khalil. "Large Language Models for Arabic Sentiment Analysis and Machine Translation." Engineering, Technology & Applied Science Research 15, no. 2 (2025): 20737–42. https://doi.org/10.48084/etasr.9584.

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Large Language Models (LLMs) have recently demonstrated outstanding performance in a variety of Natural Language Processing (NLP) tasks. Although many LLMs have been developed, only a few models have been evaluated in the context of the Arabic language, with a significant focus on the ChatGPT model. This study assessed three LLMs on two Arabic NLP tasks: sentiment analysis and machine translation. The capabilities of LLaMA, Mixtral, and Gemma under zero- and few-shot learning were investigated, and their performance was compared against State-Of-The-Art (SOTA) models. The experimental results

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Olkov, Ilya, and Alexander Gavrilov. "Visual large language models for welding assessment." Vibroengineering Procedia 58 (May 15, 2025): 314–19. https://doi.org/10.21595/vp.2025.24983.

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This paper evaluates the effectiveness of visual large language models (LLMs) for weld defect identification, focusing on their potential utility for novice welders. Using the Gemma-3B, Gemma-27B and Qwen2.5-VL-32B models, we benchmark performance against a standardized weld defect dataset and compare against a most modern version of the more traditional YOLO architecture, YOLOv12. Results show the 27B model achieves 66.36 % recall and a lower precision of 46.10 %, while the 3B model demonstrates poor reliability at 35.05 % recall, comparable to the results of the YOLOv12. Meanwhile, Qwen2.5-V

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Batth, Rituraj, Andisheh Poormassalehgoo, Kritika Bhardwaj, Elżbieta Kaniecka, and Shino Goto-Yamada. "A Simple and Scalable Chopped-Thallus Transformation Method for Marchantia polymorpha." Plants 14, no. 4 (2025): 582. https://doi.org/10.3390/plants14040582.

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The liverwort Marchantia polymorpha has emerged as a valuable model for studying fundamental biological processes and the evolutionary history of land plants. Agrobacterium-mediated transformation is widely used for genetic modification of M. polymorpha using spores, thalli, and gemmae. While spores offer high transformation efficiency, they result in diverse genetic backgrounds due to sexual reproduction. Conversely, thallus- and gemma-based methods maintain genetic consistency but are impractical for large-scale applications. To address these limitations, we developed a novel chopped-thallus

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Pendyala, Vishnu S., Neha Bais Thakur, and Radhika Agarwal. "Explainable Use of Foundation Models for Job Hiring." Electronics 14, no. 14 (2025): 2787. https://doi.org/10.3390/electronics14142787.

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Automating candidate shortlisting is a non-trivial task that stands to benefit substantially from advances in artificial intelligence. We evaluate a suite of foundation models such as Llama 2, Llama 3, Mixtral, Gemma-2b, Gemma-7b, Phi-3 Small, Phi-3 Mini, Zephyr, and Mistral-7b for their ability to predict hiring outcomes in both zero-shot and few-shot settings. Using only features extracted from applicants’ submissions, these models, on average, achieved an AUC above 0.5 in zero-shot settings. Providing a few examples similar to the job applicants based on a nearest neighbor search improved t

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Casteni, Alessia, and Elena Felicani. "LA PEDAGOGIA LINGUISTICA NELLA PROPOSTA EDUCATIVA DI GEMMA HARASIM." Italiano LinguaDue 16, no. 1 (2024): 614–40. http://dx.doi.org/10.54103/2037-3597/23863.

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La voce di Gemma Harasim, maestra fiumana, si inserisce nel panorama delle proposte didattiche per l’insegnamento della lingua tra Ottocento e Novecento: nei contributi in rivista pubblicati sui «Nuovi Doveri» e nel volume Sull’insegnamento della lingua materna, stampato nel 1906, Harasim indica tra le pagine una via di formazione linguistica a partire dall’osservazione delle cose. Il contributo si propone di portare l’attenzione su un progetto didattico innovativo: l’autrice offre «modeste discussioni didattiche», spunti di riflessione e di applicazione per costruire un metodo per l’insegname

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Lai, Yi-Pin, and Thomas R. Ioerger. "Exploiting Homoplasy in Genome-Wide Association Studies to Enhance Identification of Antibiotic-Resistance Mutations in Bacterial Genomes." Evolutionary Bioinformatics 16 (January 2020): 117693432094493. http://dx.doi.org/10.1177/1176934320944932.

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Many antibacterial drugs have multiple mechanisms of resistance, which are often represented simultaneously by a mixture of resistance mutations (some more frequent than others) in a clinical population. This presents a challenge for Genome-Wide Association Studies (GWAS) methods, making it difficult to detect less prevalent resistance mechanisms purely through (weak) statistical associations. Homoplasy, or the occurrence of multiple independent mutations at the same site, is often observed with drug resistance mutations and can be a strong indicator of positive selection. However, traditional

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Rossi, Lorenzo, Biao Lu, Mirko Reguzzoni, Daniele Sampietro, Islam Fadel, and Mark van der Meijde. "Global Moho Gravity Inversion from GOCE Data: Updates and Convergence Assessment of the GEMMA Model Algorithm." Remote Sensing 14, no. 22 (2022): 5646. http://dx.doi.org/10.3390/rs14225646.

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Since its discovery in 1909, the Moho was routinely studied by seismological methods. However, from the 1950s, a possible alternative was introduced by gravimetric inversion. Thanks to satellite gravity missions launched from the beginning of the 21st century, a global inversion became feasible, e.g., leading to the computation of the GEMMA model in 2012. This model was computed inverting the GOCE second radial derivatives of the anomalous potential by a Wiener filter, which was applied in the spherical harmonic domain, considering a two-layer model with lateral and vertical density variations

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Hoa, Tran Thai, Tran Quang Duy, Khanh Quoc Tran, and Kiet Van Nguyen. "ViFactCheck: A New Benchmark Dataset and Methods for Multi-Domain News Fact-Checking In Vietnamese." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 308–16. https://doi.org/10.1609/aaai.v39i1.32008.

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The rapid spread of information in the digital age highlights the critical need for effective fact-checking tools, particularly for languages with limited resources, such as Vietnamese. In response to this challenge, we introduce ViFactCheck, the first publicly available benchmark dataset designed specifically for Vietnamese fact-checking across multiple online news domains. This dataset contains 7,232 human-annotated pairs of claim-evidence combinations sourced from reputable Vietnamese online news, covering 12 diverse topics. It has been subjected to a meticulous annotation process to ensure

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Pingua, Bhagyajit, Adyakanta Sahoo, Meenakshi Kandpal, et al. "Medical LLMs: Fine-Tuning vs. Retrieval-Augmented Generation." Bioengineering 12, no. 7 (2025): 687. https://doi.org/10.3390/bioengineering12070687.

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Large language models (LLMs) are trained on huge datasets, which allow them to answer questions from various domains. However, their expertise is confined to the data that they were trained on. In order to specialize LLMs in niche domains like healthcare, various training methods can be employed. Two of these commonly known approaches are retrieval-augmented Generation and model fine-tuning. Five models—Llama-3.1-8B, Gemma-2-9B, Mistral-7B-Instruct, Qwen2.5-7B, and Phi-3.5-Mini-Instruct—were fine-tuned on healthcare data. These models were trained using three distinct approaches: retrieval-aug

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Castillo, Juan Manuel, Giacomo Barbieri, Alejandro Mejia, José Daniel Hernandez, and Kelly Garces. "A GEMMA-GRAFCET Generator for the Automation Software of Smart Manufacturing Systems." Machines 9, no. 10 (2021): 232. http://dx.doi.org/10.3390/machines9100232.

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Within the Industry 4.0 revolution, manufacturing enterprises are transforming to intelligent enterprises constituted by Smart Manufacturing Systems (SMSs). A key capability of SMSs is the ability to connect and communicate with each other through Industrial Internet of Things technologies, and protocols with standard syntax and semantics. In this context, the GEMMA-GRAFCET Methodology (GG-Methodology) provides a standard approach and vocabulary for the management of the Operational Modes (OMs) of SMSs through the automation software, bringing a common understanding of the exchanged data. Cons

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Hu, Junjie, Peng Wu, Yulin Li, et al. "Discovering Photoswitchable Molecules for Drug Delivery with Large Language Models and Chemist Instruction Training." Pharmaceuticals 17, no. 10 (2024): 1300. http://dx.doi.org/10.3390/ph17101300.

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Background: As large language models continue to expand in size and diversity, their substantial potential and the relevance of their applications are increasingly being acknowledged. The rapid advancement of these models also holds profound implications for the long-term design of stimulus-responsive materials used in drug delivery. Methods: The large model used Hugging Face’s Transformers package with BigBird, Gemma, and GPT NeoX architectures. Pre-training used the PubChem dataset, and fine-tuning used QM7b. Chemist instruction training was based on Direct Preference Optimization. Drug Like

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Navarro, J. A., M. E. Parés, and I. Colomina. "GEMMA: A GENERIC, EXTENSIBLE AND MODULAR MULTI-SENSOR NAVIGATION ANALYSIS SYSTEM." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 433–40. http://dx.doi.org/10.5194/isprsannals-iii-3-433-2016.

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This paper presents the concept of an architecture for a system that helps researchers in the field of Geomatics to speed up their daily research on kinematic geodesy, navigation and positioning fields. The presented ideas correspond to an extensible and modular software system aimed at the development of new navigation and positioning algorithms as well as at the evaluation of the performance of sensors. The concept, already implemented in the CTTC’s system GEMMA is generic and extensible. This means that it is possible to incorporate new navigation algorithms or sensors at no maintenance cos

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Navarro, J. A., M. E. Parés, and I. Colomina. "GEMMA: A GENERIC, EXTENSIBLE AND MODULAR MULTI-SENSOR NAVIGATION ANALYSIS SYSTEM." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-3 (June 6, 2016): 433–40. http://dx.doi.org/10.5194/isprs-annals-iii-3-433-2016.

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This paper presents the concept of an architecture for a system that helps researchers in the field of Geomatics to speed up their daily research on kinematic geodesy, navigation and positioning fields. The presented ideas correspond to an extensible and modular software system aimed at the development of new navigation and positioning algorithms as well as at the evaluation of the performance of sensors. The concept, already implemented in the CTTC’s system GEMMA is generic and extensible. This means that it is possible to incorporate new navigation algorithms or sensors at no maintenance cos

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Wang, Meiyue, Ruidong Li, and Shizhong Xu. "Deshrinking ridge regression for genome-wide association studies." Bioinformatics 36, no. 14 (2020): 4154–62. http://dx.doi.org/10.1093/bioinformatics/btaa345.

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Abstract Motivation Genome-wide association studies (GWAS) are still the primary steps toward gene discovery. The urgency is more obvious in the big data era when GWAS are conducted simultaneously for thousand traits, e.g. transcriptomic and metabolomic traits. Efficient mixed model association (EMMA) and genome-wide efficient mixed model association (GEMMA) are the widely used methods for GWAS. An algorithm with high computational efficiency is badly needed. It is interesting to note that the test statistics of the ordinary ridge regression (ORR) have the same patterns across the genome as th

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Filho, Nelson Ribeiro, Cristiano Mendel Martins, and Renata de Sena Santos. "A NOVEL REGIONAL-RESIDUAL SEPARATION APPROACH FOR GRAVITY DATA THROUGH CRUSTAL MODELING." Revista Brasileira de Geofísica 36, no. 4 (2018): 1. http://dx.doi.org/10.22564/rbgf.v36i4.1980.

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ABSTRACT. Gravity anomalies normally contain information of all sources beneath Earth’s surface. Once residual anomalies exhibit information about the main target, the knowledge of this specific residual signal is extremely important to interpretation. To find this signal, it’s necessary to perform regional-residual separation. We present here a new approach of separation by using gravity crustal modeling. We divide the surface in prisms, with density given by GEMMA. We calculate the regional signal, assuming Earth’s crust can be the source of observed anomaly. This methodology was applied on

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Surya N A, Karthik. "Towards Intelligent Legal Information Retrieval a Transformer Based Framework." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1`—9. https://doi.org/10.55041/ijsrem48260.

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Abstract — In the evolving landscape of legal technology, retrieving relevant laws and case judgments efficiently remains a critical challenge due to the complexity, ambiguity, and contextual nature of legal language. Traditional keyword-based legal search engines often fail to capture the semantic relevance required for precise legal reasoning. This paper introduces a modern Transformer-based Legal Information Retrieval System tailored to the Indian legal domain, leveraging Retrieval-Augmented Generation (RAG) architecture. The proposed system integrates Google's Gemma 2B-IT language model wi

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Frederickson, Stephen C., Mark D. Steinmiller, Tiffany Rae Blaylock, et al. "Comparison of Juvenile Feed Protocols on Growth and Spawning in Zebrafish." Journal of the American Association for Laboratory Animal Science 60, no. 3 (2021): 298–305. http://dx.doi.org/10.30802/aalas-jaalas-20-000105.

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Over the past 2 decades, zebrafish, Danio rerio, have become a mainstream laboratory animal model, yet zebrafish husbandry practices remain far from standardized. Feeding protocols play a critical role in the health, wellbeing, and productivity of zebrafish laboratories, yet they vary significantly between facilities. In this study, we compared our current feeding protocol for juvenile zebrafish (30 dpf to 75 dpf), a 3:1mixture of fish flake and freeze-dried krill fed twice per day with live artemia twice per day (FKA), to a diet of Gemma Micro 300 fed once per day with live artemia once per d

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Sri Setiawati and Ahmad Muhammad Thantawi. "Implementasi Aplikasi Kaskita Untuk Manajemen Pembukuan Keuangan Menggunakan Pendekatan User-Centered Design (Ucd) Pada Pelaku Umkm Gemma Indonesia." IKRAITH-EKONOMIKA 7, no. 3 (2024): 266–74. http://dx.doi.org/10.37817/ikraith-ekonomika.v7i3.4426.

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urgensi penelitian ini terletak pada kemampuannya untuk memberikan solusi nyata bagi UMKM dalam meningkatkan manajemen keuangannya, sekaligus mendukung visi GEMMA Indonesia dalam membangun ekosistem UMKM yang lebih tangguh dan berkelas. Penelitian ini tidak hanya memberikan manfaat praktis secara langsung bagi UMKM yang terlibat, tetapi juga memiliki potensi untuk menjadi model bagi UMKM lain di Indonesia dalam mengadopsi teknologi untuk pertumbuhan dan keberlangsungan usaha. Tujuan: Penelitian ini bertujuan untuk mengeksplorasi implementasi Aplikasi Kaskita menggunakan pendekatan User-Centere

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Dobeš, Marek. "Evaluation of Quality of Slovak Language Use in LLMS." Acta Electrotechnica et Informatica 25, no. 1 (2025): 28–33. https://doi.org/10.2478/aei-2025-0004.

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Abstract This study explores the rapid advancement of Large Language Models (LLMs) and their growing support for languages beyond English. Despite this progress, the LLM ecosystem remains predominantly focused on English, particularly in key areas such as tokenizers and evaluation metrics. Traditional metrics like BLEU and ROUGE, designed for English, are less effective for languages with different linguistic structures, such as Slovak. This paper highlights the limitations of current metrics and the need for language-specific evaluation methods. The performance of three LLMs—Mistral 7B, Mistr

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Gupta, Ojasvi, Stefano Marrone, Francesco Gargiulo, Rajesh Jaiswal, and Lidia Marassi. "Understanding Social Biases in Large Language Models." AI 6, no. 5 (2025): 106. https://doi.org/10.3390/ai6050106.

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Background/Objectives: Large Language Models (LLMs) like ChatGPT, LLAMA, and Mistral are widely used for automating tasks such as content creation and data analysis. However, due to their training on publicly available internet data, they may inherit social biases. We aimed to investigate the social biases (i.e., ethnic, gender, and disability biases) in these models and evaluate how different model versions handle them. Methods: We instruction-tuned popular models (like Mistral, LLAMA, and Gemma), and for this we curated a dataset constructed by collecting and modifying diverse data from vari

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Nedashkovskaya, N. I., and R. I. Yeremichuk. "EVALUATION OF QUANTIZED LARGE LANGUAGE MODELS IN THE TEXT SUMMARIZATION PROBLEM." Radio Electronics, Computer Science, Control, no. 2 (June 29, 2025): 133–47. https://doi.org/10.15588/1607-3274-2025-2-12.

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Context. The problem of increasing the efficiency of deep artificial neural networks in terms of memory and energy consumption, and the multi-criteria evaluation of the quality of the results of large language models (LLM) taking into account the judgments of users in the task of summarizing texts, are considered. The object of the study is the process of automated text summarization based on LLMs.Objective. The goal of the work is to find a compromise between the complexity of the LLM, its performance and operational efficiency in text summarization problem.Method. An LLM evaluation algorithm

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Piyasawetkul, Thanawat, Suppachai Tiyaworanant, and Tarapong Srisongkram. "AppHerb: Language Model for Recommending Traditional Thai Medicine." AI 6, no. 8 (2025): 170. https://doi.org/10.3390/ai6080170.

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Trust in Traditional Thai Medicine (TTM) among Thai people has been reduced due to a lack of objective standards and the susceptibility of the general population to false information. The emergence of generative artificial intelligence (Gen AI) has significantly impacted various industries, including traditional medicine. However, previous Gen AI models have primarily focused on prescription generation based on Traditional Chinese Medicine (TCM), leaving TTM unexplored. To address this gap, we propose a novel fast-learning fine-tuned language model fortified with TTM knowledge. We utilized tex

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Gu, Da Cheng, and Wei Liu. "Assessing Vulnerabilities in State-of-the-Art Large Language Models Through Hex Injection (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29377–78. https://doi.org/10.1609/aaai.v39i28.35257.

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State-of-the-art large language models (LLMs) are designed with robust safeguards to prevent the disclosure of harmful information and dangerous procedures. However, "jailbreaking" techniques can circumvent these protections by exploiting vulnerabilities in the models. This paper introduces a novel method, Hex Injection, which leverages a specific weakness in LLMs' ability to decode encoded text to uncover concealed dangerous instructions. Hex Injection distinguishes itself from traditional methods by combining encoded instructions with plaintext prompts to reveal unsafe content more effective

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Khan, Muhammad Junaid, and Gita Sukthankar. "SC-Phi2: A Fine-Tuned Small Language Model for StarCraft II Build Order Prediction." AI 5, no. 4 (2024): 2338–52. http://dx.doi.org/10.3390/ai5040115.

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Background: This article introduces SC-Phi2, a fine-tuned StarCraft II small language model. Small language models, like Phi2, Gemma, and DistilBERT, are streamlined versions of large language models (LLMs) with fewer parameters that require less computational power and memory to run. Method: To teach Microsoft’s Phi2 model about StarCraft, we create a new SC2 text dataset with information about StarCraft races, roles, and actions and use it to fine-tune Phi-2 with self-supervised learning. We pair this language model with a Vision Transformer (ViT) from the pre-trained BLIP-2 (Bootstrapping L

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Liu, Jin-Yang, Ya-Wen Zhang, Xu Han, et al. "An evolutionary population structure model reveals pleiotropic effects of GmPDAT for traits related to seed size and oil content in soybean." Journal of Experimental Botany 71, no. 22 (2020): 6988–7002. http://dx.doi.org/10.1093/jxb/eraa426.

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Abstract Seed oil traits in soybean that are of benefit to human nutrition and health have been selected for during crop domestication. However, these domesticated traits have significant differences across various evolutionary types. In this study, we found that the integration of evolutionary population structure (evolutionary types) with genome-wide association studies increased the power of gene detection, and it identified one locus for traits related to seed size and oil content on chromosome 13. This domestication locus, together with another one in a 200-kb region, was confirmed by the

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Papoulias, D. K., and T. S. Kosmas. "Probing electromagnetic neutrino properties within the tensor non-standard neutrino-nucleus interactions." HNPS Proceedings 23 (March 8, 2019): 119. http://dx.doi.org/10.12681/hnps.1915.

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Non-standard coherent neutrino scattering off nuclei is extensively studied through realistic nuclear structure calculations performed within the framework of the quasi-particle random phase approximation (QRPA). More specifically, we focus on the accurate estimation of the number of events expected to be measured by the COHERENT experiment at the Spallation Neutron Source at Oak Ridge, as well as by the reactor neutrino experiments TEXONO and GEMMA. To this purpose our study concentrates on the relevant detector materials 20 Ne, 40 Ar, 76 Ge and 132 Xe. In this context, we obtain stringent co

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Yoo, Yun Jae, Ki H. Oh, Luke A. Torre-Healy, and Richard A. Moffitt. "Abstract A058: Meta-analysis of single-cell RNA expression in genetically engineered mouse models of pancreatic ductal adenocarcinoma reveals inter-model heterogeneity." Cancer Research 82, no. 22_Supplement (2022): A058. http://dx.doi.org/10.1158/1538-7445.panca22-a058.

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Abstract Background: Genetically engineered mouse models (GEMMs) are widely used in the study of pancreatic ductal adenocarcinoma (PDAC) because of their immune-competent tumor microenvironment (TME); however, the extent to which particular GEMMs recapitulate the tumor and TME observed in the patient population has not been systematically evaluated. In this study, we integrate single-cell RNA sequencing (sc-RNA-seq) data from multiple studies and multiple GEMM backgrounds to identify differences in the cellular compositions of popular PDAC GEMMs. Methods: A total of 49,191 cells were used from

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Tian, Liang, and Shiguang Jia. "Distribution-Aware Dual-LLM Collaborative Framework for Human Preference Prediction." Applied and Computational Engineering 109, no. 1 (2024): 150–58. http://dx.doi.org/10.54254/2755-2721/2024.17896.

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This paper proposes a novel Distribution-aware Dual-LLM Collaborative Framework (D2CF) for human preference prediction in large language model dialogue systems. Through data analysis in the Kaggle LMSYS Chatbot Arena competition, we innovatively selected two complementary base models: Gemma-2-9b and Llama-3.1-8b. The framework's main technical innovations include: (1) A model complementarity quantification method based on Wasserstein distance, optimizing model selection from a data distribution perspective; (2) A parameter-efficient QLoRA improvement strategy that reduced computational overhea

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Singh, Shiv. "Building Smarter End-Of-Turn Detection for Conversational AI Using Transformer-Based Semantic Models." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47748.

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Abstract This paper presents a novel approach to end-of-turn detection for conversational AI systems, combining traditional voice activity detection (VAD) with semantic understanding through a transformer-based model. End-of-turn detection remains one of the most challenging aspects of creating natural conversational AI interfaces, as current systems rely primarily on silence thresholds that fail to capture the semantic cues humans use to determine speaking turns. Our approach leverages a lightweight transformer model based on the Gemma-3-1b architecture that analyzes transcribed speech in rea

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Ren, Wenlong, Zhikai Liang, Shu He, and Jing Xiao. "Hybrid of Restricted and Penalized Maximum Likelihood Method for Efficient Genome-Wide Association Study." Genes 11, no. 11 (2020): 1286. http://dx.doi.org/10.3390/genes11111286.

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In genome-wide association studies, linear mixed models (LMMs) have been widely used to explore the molecular mechanism of complex traits. However, typical association approaches suffer from several important drawbacks: estimation of variance components in LMMs with large scale individuals is computationally slow; single-locus model is unsatisfactory to handle complex confounding and causes loss of statistical power. To address these issues, we propose an efficient two-stage method based on hybrid of restricted and penalized maximum likelihood, named HRePML. Firstly, we performed restricted ma

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Wang, Yaolin, Heng Zhang, Sheng Zhu, Tengfei Shen, Huixin Pan, and Meng Xu. "Association Mapping and Expression Analysis of the Genes Involved in the Wood Formation of Poplar." International Journal of Molecular Sciences 24, no. 16 (2023): 12662. http://dx.doi.org/10.3390/ijms241612662.

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Xylogenesis is a complex and sequential biosynthetic process controlled by polygenes. Deciphering the genetic architecture of this complex quantitative trait could provide valuable information for increasing wood biomass and improving its properties. Here, we performed genomic resequencing of 64 24-year-old trees (64 hybrids of section Aigeiros and their parents) grown in the same field and conducted full-sib family-based association analyses of two growth and six woody traits using GEMMA as a choice of association model selection. We identified 1342 significantly associated single nucleotide

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Shen, Ziyuan, Chenlu He, Haimiao Chen, Lishun Xiao, Yingliang Jin, and Shuiping Huang. "Potential Genes Associated with the Survival of Lung Adenocarcinoma Were Identified by Methylation." Computational and Mathematical Methods in Medicine 2020 (November 18, 2020): 1–13. http://dx.doi.org/10.1155/2020/7103412.

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Background. Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. The purpose of this study is to search for genes related to the prognosis of LUAD through methylation based on a linear mixed model (LMM). Methods. Gene expression, methylation, and survival data of LUAD patients were downloaded from the TCGA database. Based on the LMM model, the GEMMA algorithm was used to screen the predictive genes related to LUAD survival. The Cox model was used to further screen the predicted genes, and then, protein-protein interaction (PPI) network was constructed. Through the so

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Kanapin, Alexander, Tatyana Rozhmina, Mikhail Bankin, et al. "Genetic Determinants of Fiber-Associated Traits in Flax Identified by Omics Data Integration." International Journal of Molecular Sciences 23, no. 23 (2022): 14536. http://dx.doi.org/10.3390/ijms232314536.

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In this paper, we explore potential genetic factors in control of flax phenotypes associated with fiber by mining a collection of 306 flax accessions from the Federal Research Centre of the Bast Fiber Crops, Torzhok, Russia. In total, 11 traits were assessed in the course of 3 successive years. A genome-wide association study was performed for each phenotype independently using six different single-locus models implemented in the GAPIT3 R package. Moreover, we applied a multivariate linear mixed model implemented in the GEMMA package to account for trait correlations and potential pleiotropic

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Garreau, Hervé, Yann Labrune, Hervé Chapuis, et al. "Genome wide association study of growth and feed efficiency traits in rabbits." World Rabbit Science 31, no. 3 (2023): 163–69. http://dx.doi.org/10.4995/wrs.2023.18215.

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Feed efficiency is a major production trait in animal genetic breeding schemes. To further investigate the genetic control of feed efficiency in rabbits, we performed a genome-wide association study (GWAS) for growth and feed efficiency on 679 rabbits genotyped with the Affimetrix Axiom Rabbit 200K Genotyping Array. After quality control, 127 847 single-nucleotide polymorphisms (SNP) were retained for association analyses. The GWAS were performed using GEMMA software, applying a mixed univariate animal model with a linear regression on each SNP allele. The traits analysed were weight at weanin

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Meram, Muhammet Bora, Çağatay Kalkan, Tuğba Çelikten, and Aytuğ Onan. "GPT vs. Other Large Language Models for Topic Modeling: A Comprehensive Comparison." ICCK Transactions on Emerging Topics in Artificial Intelligence 2, no. 3 (2025): 116. https://doi.org/10.62762/tetai.2025.871572.

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Topic modeling is a widely used unsupervised natural language processing (NLP) technique aimed at discovering latent themes within documents. Since traditional methods fall short in capturing contextual meaning, approaches based on large language models (LLMs)—such as BERTopic—hold the potential to generate more meaningful and diverse topics. However, systematic comparative studies of these models, especially in domains requiring high accuracy and interpretability such as healthcare, remain limited. This study compares ten different LLMs (GPT, Claude, Gemini, LLaMA, Qwen, Phi, Zephyr, DeepSeek

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Huo, Wenshuai, Xiaocheng Feng, Yichong Huang, et al. "Enhancing Non-English Capabilities of English-Centric Large Language Models Through Deep Supervision Fine-Tuning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24185–93. https://doi.org/10.1609/aaai.v39i23.34594.

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Large language models (LLMs) have demonstrated significant progress in multilingual language understanding and generation. However, due to the imbalance in training data, their capabilities in non-English languages are limited. Recent studies revealed the English-pivot multilingual mechanism of LLMs, where LLMs implicitly convert non-English queries into English ones at the bottom layers and adopt English for thinking at the middle layers. However, due to the absence of explicit supervision for cross-lingual alignment in the intermediate layers of LLMs, the internal representations during thes

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Zhuang, Richard, Akshat Gupta, Richard Yang, Aniket Rahane, Zhengyu Li, and Gopala Anumanchipalli. "PokerBench: Training Large Language Models to Become Professional Poker Players." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 24 (2025): 26175–82. https://doi.org/10.1609/aaai.v39i24.34814.

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We introduce PokerBench - a benchmark for evaluating the poker-playing abilities of large language models (LLMs). As LLMs excel in traditional NLP tasks, their application to complex, strategic games like poker poses a new challenge. Poker, an incomplete information game, demands a multitude of skills such as mathematics, reasoning, planning, strategy, and a deep understanding of game theory and human psychology. This makes Poker the ideal next frontier for large language models. PokerBench consists of a comprehensive compilation of 11,000 most important scenarios, split between pre-flop and p

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Kravitz, Amanda, Mingsi Liao, Gota Morota, et al. "Retrospective Single Nucleotide Polymorphism Analysis of Host Resistance and Susceptibility to Ovine Johne’s Disease Using Restored FFPE DNA." International Journal of Molecular Sciences 25, no. 14 (2024): 7748. http://dx.doi.org/10.3390/ijms25147748.

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Johne’s disease (JD), also known as paratuberculosis, is a chronic, untreatable gastroenteritis of ruminants caused by Mycobacterium avium subsp. paratuberculosis (MAP) infection. Evidence for host genetic resistance to disease progression exists, although it is limited due to the extended incubation period (years) and diagnostic challenges. To overcome this, previously restored formalin-fixed paraffin embedded tissue (FFPE) DNA from archived FFPE tissue cassettes was utilized for a novel retrospective case-control genome-wide association study (GWAS) on ovine JD. Samples from known MAP-infect

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Pathmanathan, Pankayaraj, Souradip Chakraborty, Xiangyu Liu, Yongyuan Liang, and Furong Huang. "Is Poisoning a Real Threat to DPO? Maybe More So Than You Think." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 26 (2025): 27556–64. https://doi.org/10.1609/aaai.v39i26.34968.

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Recent advancements in Reinforcement Learning with Human Feedback (RLHF) have significantly impacted the alignment of Large Language Models (LLMs). The sensitivity of reinforcement learning algorithms such as Proximal Policy Optimization (PPO) has led to new line work on Direct Preference Optimization (DPO), which treats RLHF in a supervised learning framework. The increased practical use of these RLHF methods warrants an analysis of their vulnerabilities. In this work, we investigate the vulnerabilities of DPO to poisoning attacks under different scenarios and compare the effectiveness of pre

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Chunduri, Alisha, Pamela M. Watson, and David G. Ashbrook. "New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GeneNetwork." Genes 13, no. 4 (2022): 614. http://dx.doi.org/10.3390/genes13040614.

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Gene-by-environment interactions are important for all facets of biology, especially behaviour. Families of isogenic strains of mice, such as the BXD strains, are excellently placed to study these interactions, as the same genome can be tested in multiple environments. BXD strains are recombinant inbred mouse strains derived from crossing two inbred strains—C57BL/6J and DBA/2J mice. Many reproducible genometypes can be leveraged, and old data can be reanalysed with new tools to produce novel insights. We obtained drug and behavioural phenotypes from Philip et al. Genes, Brain and Behaviour 201

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POGORILYY, S., and P. V. BILETSKY. "THE DEVELOPMENT OF METHODS FOR COREFERENCE RESOLUTION IN UKRAINIAN TEXTS BASED ON LARGE LANGUAGE MODELS." Scientific papers of Donetsk National Technical University. Series: Informatics, Cybernetics and Computer Science 1, no. 40 (2025): 152–27. https://doi.org/10.31474/1996-1588-2025-1-40-15-27.

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"This work investigates the task of automated Coreference Resolution in Ukrainian texts – a key problem in Natural Language Processing (NLP) necessary for deep semantic analysis, information extraction, machine translation, and other applications. Extended examples of coreference are provided, emphasizing the importance of the task and the specific difficulties of solving it for the Ukrainian language, primarily due to its free word order. The Transformer architecture, which underlies modern Large Language Models (LLMs), is discussed. The key characteristics of several state-of-the-art large l

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SHARAT CHANDRA, PURIPANDA. "Fine-Tuning Small LLMs for High-Quality Semantic Search: A Cost-Efficient Alternative to Foundation Models." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49678.

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Abstract - Large language models (LLMs) have demonstrated remarkable performance in natural language understanding, yet their deployment for real-time semantic search and recommendation tasks remains impractical due to significant computational demands. This paper introduces a cost-efficient framework for fine-tuning small-scale models tailored for high-quality semantic movie recommendation. We leverage Gemma 3, a compact generative model, to produce enriched natural language descriptions of movies from structured metadata, and Granite Embedder, a lightweight transformer-based encoder, to comp

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Liu, Ling-Ling, Bin Chen, Sheng-Lei Chen, and Wu-Jun Liu. "A Genome-Wide Association Study of the Chest Circumference Trait in Xinjiang Donkeys Based on Whole-Genome Sequencing Technology." Genes 14, no. 5 (2023): 1081. http://dx.doi.org/10.3390/genes14051081.

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Animal genotyping by means of genome-wide association studies is important for connecting phenotypes of interest with their underlying genetics in livestock. However, the use of whole genome sequencing to investigate chest circumference (CC) in donkeys has rarely been reported. We aimed to use the genome-wide association study approach to detect significant single nucleotide polymorphisms (SNPs) and key genes associated with chest circumference traits in Xinjiang donkeys. We assessed 112 Xinjiang donkeys in this study. The chest circumference of each was measured 2 h before milking. We re-sequ

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McFadden, David G., Katerina Politi, Arjun Bhutkar, et al. "Mutational landscape of EGFR-, MYC-, and Kras-driven genetically engineered mouse models of lung adenocarcinoma." Proceedings of the National Academy of Sciences 113, no. 42 (2016): E6409—E6417. http://dx.doi.org/10.1073/pnas.1613601113.

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Genetically engineered mouse models (GEMMs) of cancer are increasingly being used to assess putative driver mutations identified by large-scale sequencing of human cancer genomes. To accurately interpret experiments that introduce additional mutations, an understanding of the somatic genetic profile and evolution of GEMM tumors is necessary. Here, we performed whole-exome sequencing of tumors from three GEMMs of lung adenocarcinoma driven by mutant epidermal growth factor receptor (EGFR), mutant Kirsten rat sarcoma viral oncogene homolog (Kras), or overexpression of MYC proto-oncogene. Tumors

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Xu, Gaowen, Yujing Cheng, Xiaoqiu Wang, et al. "Identification of Single Nucleotide Polymorphic Loci and Candidate Genes for Seed Germination Percentage in Okra under Salt and No-Salt Stresses by Genome-Wide Association Study." Plants 13, no. 5 (2024): 588. http://dx.doi.org/10.3390/plants13050588.

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Excessive soil salinity is a major stressor inhibiting crops’ growth, development, and yield. Seed germination is a critical stage of crop growth and development, as well as one of the most salt-sensitive stages. Salt stress has a significant inhibitory effect on seed germination. Okra is a nutritious vegetable, but its seed germination percentage (GP) is low, whether under salt stress conditions or suitable conditions. In this study, we used 180 okra accessions and conducted a genome-wide association study (GWAS) on the germination percentage using 20,133,859 single nucleotide polymorphic (SN

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