We introduce Goat, a fine-tuned LLaMA model that significantly outperforms GPT-4 on a range of arithmetic tasks. Fine-tuned on a synthetically generated dataset, Goat achieves state-of-the-art performance on BIG-bench arithmetic sub-task. In particular, the zero-shot Goat-7B matches or even surpasses the accuracy achieved by the few-shot PaLM-540B. Surprisingly, Goat can achieve near-perfect accuracy on large-number addition and subtraction through supervised fine-tuning only, which is almost impossible with previous pretrained language models, such as Bloom, OPT, GPT-NeoX, etc. We attribute Goat's exceptional performance to LLaMA's consistent tokenization of numbers. To tackle more challenging tasks like large-number multiplication and division, we propose an approach that classifies tasks based on their learnability, and subsequently decomposes unlearnable tasks, such as multi-digit multiplication and division, into a series of learnable tasks by leveraging basic arithmetic principles. We thoroughly examine the performance of our model, offering a comprehensive evaluation of the effectiveness of our proposed decomposition steps. Additionally, Goat-7B can be easily trained using LoRA on a 24GB VRAM GPU, facilitating reproducibility for other researchers. We release our model, dataset, and the Python script for dataset generation.
A comprehensive study of LLM-based argument classification: from Llama through DeepSeek to GPT-5.2
Faculty of Computer Science, Faculty of Humanities（计算机科学系，人文科学系）；Institute of Mathematics Faculty of Humanities（人文科学学院数学研究所）；University of Silesia（斯蒂利亚大学）；AGH University of Krakow（克拉科夫AGH大学）
Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Models
Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；University of Texas Health（德克萨斯大学健康科学中心）；Virginia Hospital Center（弗吉尼亚医院中心）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）；Stanford University（斯坦福大学）；Cedars-Sinai Medical Center（西达赛奈医疗中心）；Inova Fairfax Medical Campus（伊诺瓦费尔法克斯医疗中心）；University of California–Los Angeles（加州大学洛杉矶分校）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Columbia University（哥伦比亚大学）
MGH Radiology Llama: A Llama 3 70B Model for Radiology
School of Computing, University of Georgia（佐治亚大学计算学院）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（麻省总医院及哈佛医学院放射科）
A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1
Faculty of Computer Science, Electronics, and Telecommunications AGH University of Krakow（计算机科学、电子与电信学院AGH克拉科夫大学）；Faculty of Humanities AGH University of Krakow（人文学院AGH克拉科夫大学）；Institute of Mathematics University of Silesia（数学学院桑特学院）
A Multi-Agent Framework for Medical AI: Leveraging Fine-Tuned GPT, LLaMA, and DeepSeek R1 for Evidence-Based and Bias-Aware Clinical Query Processing
Department of Computing；Games, Teesside University, Middlesbrough, United Kingdom Centre for Digital Innovation, Teesside University, Middlesbrough, United Kingdom Faculty of Business \& Technology, University of Sunderland, Sunderland, United Kingdom
HPC-GPT: Integrating Large Language Model for High-Performance Computing
University of California, Merced（加州大学默塞德分校）；Argonne National Laboratory（阿贡国家实验室）；Iowa State University（爱荷华州立大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Binghamton University（宾汉姆顿大学）
GP-GPT: Large Language Model for Gene-Phenotype Mapping
Department of Computer Science and Engineering, The University of Texas at Arlington（计算机科学与工程系，德克萨斯理工大学 Arlington 分校）；School of Computing, University of Georgia（计算学院，佐治亚大学）；Department of Computer Science, Indiana University Indianapolis（计算机科学系，印第安纳大学 Indianapolis 分校）；Department of Radiology, Massachusetts General Hospital and Harvard Medical School（放射科，麻省总医院和哈佛医学院）；Department of Statistics, University of Georgia（统计系，佐治亚大学）；Department of Epidemiology & Biostatistics, University of Georgia（流行病学与生物统计学系，佐治亚大学）；Department of Environmental Health Science, University of Georgia（环境流行病学系，佐治亚大学）
Is Open-Source There Yet? A Comparative Study on Commercial and Open-Source LLMs in Their Ability to Label Chest X-Ray Reports
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（麻省总医院与哈佛医学院 Athinoula A. Martinos 生物医学成像中心）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Dana-Farber Cancer Institute（丹娜法伯癌症研究院）
Heuristics and Biases in AI Decision-Making: Implications for Responsible AGI
Golisano Institute for Sustainability Rochester Institute of Technology Rochester, The United States；Information Sciences \& Technology, CEHC University at Albany, SUNY Albany, The United States
Evaluating Modern Large Language Models on Low-Resource and Morphologically Rich Languages:A Cross-Lingual Benchmark Across Cantonese, Japanese, and Turkish
University of California, Santa Cruz（加州大学圣克鲁兹分校）；Carnegie Mellon University（卡内基梅隆大学）；University of Pittsburgh（匹兹堡大学）
Benchmarking the Performance of Pre-trained LLMs across Urdu NLP Tasks
Center for Language Engineering（语言工程中心）；Al-Khawarizmi Institute of Computer Science（阿尔-花剌子密计算机科学研究所）；University of Engineering and Technology Lahore（拉合尔工程技术大学）
Desert Camels and Oil Sheikhs: Arab-Centric Red Teaming of Frontier LLMs
MBZUAI（穆罕默德·本·扎耶德人工智能大学）；University of Khartoum（喀土穆大学）；Universität des Saarlandes（萨尔大学）；University of British Columbia（不列颠哥伦比亚大学）；Invertible AI（可逆人工智能公司）；Vector Institute（向量研究所）
Cancer Vaccine Adjuvant Name Recognition from Biomedical Literature using Large Language Models
University of North Dakota School of Medicine and Health Sciences（北达科他大学医学与健康科学学院）；University of North Dakota（北达科他大学）；University of Michigan（密歇根大学）；Bogazici University（博阿齐奇大学）
