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However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans. Starting with an initial set of instructions, we use our proposed Evol-Instruct to rewrite them step by step into more complex instructions. Then, we mix all generated instruction data to fine-tune LLaMA. We call the resulting model WizardLM. Human evaluations on a complexity-balanced test bed and Vicuna's testset show that instructions from Evol-Instruct are superior to human-create","suggestedLayer":"paper","snapshotGeneratedAt":"2026-07-04T15:25:15.984Z"},"createdAt":"2026-07-05T07:10:14.444Z"},{"id":"candidate_entity_arxiv_paper_2307_02288","entityId":"paper_arxiv_2307_02288","type":"paper","name":"Performance Comparison of Large Language Models on VNHSGE English Dataset: OpenAI ChatGPT, Microsoft Bing Chat, and Google Bard","layer":"ai_infra","description":"arXiv paper candidate related to OpenAI; published 2023-07-05T13:40:57Z.","websiteUrl":"https://arxiv.org/abs/2307.02288v3","country":"","statusText":"preprint","valuation":"N/A","aliases":["Performance Comparison of Large Language Models on VNHSGE English Dataset: OpenAI ChatGPT, Microsoft Bing Chat, and Google Bard","2307.02288","cs.CL","cs.HC"],"confidence":0.6,"evidenceUrl":"https://arxiv.org/abs/2307.02288v3","extractionMethod":"arxiv_paper_snapshot","status":"candidate","payload":{"note":"arXiv paper candidate; review relevance, title, authors and topic categories before approving as a paper/research artifact entity.","sourceId":"src_arxiv","associatedEntityId":"openai","associatedEntityName":"OpenAI","arxivPaperId":"2307.02288","arxivUrl":"https://arxiv.org/abs/2307.02288v3","pdfUrl":"https://arxiv.org/pdf/2307.02288v3","normalizedArxivPaperId":"2307.02288","normalizedTitleKey":"performance comparison of large language models on vnhsge english dataset openai chatgpt microsoft bing chat and google bard","dedupKeys":["arxiv:2307.02288","title:performance comparison of large language models on vnhsge english dataset openai chatgpt microsoft bing chat and google bard","source:arxiv:title:performance comparison of large language models on vnhsge english dataset openai chatgpt microsoft bing chat and google bard"],"published":"2023-07-05T13:40:57Z","updated":"2023-07-20T01:13:27Z","authors":["Xuan-Quy Dao"],"categories":["cs.CL","cs.HC"],"summary":"This paper presents a performance comparison of three large language models (LLMs), namely OpenAI ChatGPT, Microsoft Bing Chat (BingChat), and Google Bard, on the VNHSGE English dataset. The performance of BingChat, Bard, and ChatGPT (GPT-3.5) is 92.4\\%, 86\\%, and 79.2\\%, respectively. The results show that BingChat is better than ChatGPT and Bard. Therefore, BingChat and Bard can replace ChatGPT while ChatGPT is not yet officially available in Vietnam. The results also indicate that BingChat, Bard and ChatGPT outperform Vietnamese students in English language proficiency. The findings of this study contribute to the understanding of the potential of LLMs in English language education. The remarkable performance of ChatGPT, BingChat, and Bard demonstrates their potential as effective tools","suggestedLayer":"paper","snapshotGeneratedAt":"2026-07-04T15:25:15.984Z"},"createdAt":"2026-07-05T07:10:14.444Z"},{"id":"candidate_entity_arxiv_paper_2310_10449","entityId":"paper_arxiv_2310_10449","type":"paper","name":"Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Models","layer":"ai_infra","description":"arXiv paper candidate related to OpenAI; published 2023-10-16T14:33:02Z.","websiteUrl":"https://arxiv.org/abs/2310.10449v2","country":"","statusText":"preprint","valuation":"N/A","aliases":["Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Models","2310.10449","cs.CL","cs.AI","cs.LG"],"confidence":0.6,"evidenceUrl":"https://arxiv.org/abs/2310.10449v2","extractionMethod":"arxiv_paper_snapshot","status":"candidate","payload":{"note":"arXiv paper candidate; review relevance, title, authors and topic categories before approving as a paper/research artifact entity.","sourceId":"src_arxiv","associatedEntityId":"openai","associatedEntityName":"OpenAI","arxivPaperId":"2310.10449","arxivUrl":"https://arxiv.org/abs/2310.10449v2","pdfUrl":"https://arxiv.org/pdf/2310.10449v2","normalizedArxivPaperId":"2310.10449","normalizedTitleKey":"text summarization using large language models a comparative study of mpt 7b instruct falcon 7b instruct and openai chat gpt models","dedupKeys":["arxiv:2310.10449","title:text summarization using large language models a comparative study of mpt 7b instruct falcon 7b instruct and openai chat gpt models","source:arxiv:title:text summarization using large language models a comparative study of mpt 7b instruct falcon 7b instruct and openai chat gpt models"],"published":"2023-10-16T14:33:02Z","updated":"2023-10-17T19:54:16Z","authors":["Lochan Basyal","Mihir Sanghvi"],"categories":["cs.CL","cs.AI","cs.LG"],"summary":"Text summarization is a critical Natural Language Processing (NLP) task with applications ranging from information retrieval to content generation. Leveraging Large Language Models (LLMs) has shown remarkable promise in enhancing summarization techniques. This paper embarks on an exploration of text summarization with a diverse set of LLMs, including MPT-7b-instruct, falcon-7b-instruct, and OpenAI ChatGPT text-davinci-003 models. The experiment was performed with different hyperparameters and evaluated the generated summaries using widely accepted metrics such as the Bilingual Evaluation Understudy (BLEU) Score, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) Score, and Bidirectional Encoder Representations from Transformers (BERT) Score. According to the experiment, text-davinci","suggestedLayer":"paper","snapshotGeneratedAt":"2026-07-04T15:25:15.984Z"},"createdAt":"2026-07-05T07:10:14.444Z"},{"id":"candidate_entity_arxiv_paper_2402_13499","entityId":"paper_arxiv_2402_13499","type":"paper","name":"Benchmarking and Dissecting the Nvidia Hopper GPU Architecture","layer":"ai_infra","description":"arXiv paper candidate related to NVIDIA; published 2024-02-21T03:21:29Z.","websiteUrl":"https://arxiv.org/abs/2402.13499v1","country":"","statusText":"preprint","valuation":"N/A","aliases":["Benchmarking and Dissecting the Nvidia Hopper GPU Architecture","2402.13499","cs.AR"],"confidence":0.6,"evidenceUrl":"https://arxiv.org/abs/2402.13499v1","extractionMethod":"arxiv_paper_snapshot","status":"candidate","payload":{"note":"arXiv paper candidate; review relevance, title, authors and topic categories before approving as a paper/research artifact entity.","sourceId":"src_arxiv","associatedEntityId":"nvidia","associatedEntityName":"NVIDIA","arxivPaperId":"2402.13499","arxivUrl":"https://arxiv.org/abs/2402.13499v1","pdfUrl":"https://arxiv.org/pdf/2402.13499v1","normalizedArxivPaperId":"2402.13499","normalizedTitleKey":"benchmarking and dissecting the nvidia hopper gpu architecture","dedupKeys":["arxiv:2402.13499","title:benchmarking and dissecting the nvidia hopper gpu architecture","source:arxiv:title:benchmarking and dissecting the nvidia hopper gpu architecture"],"published":"2024-02-21T03:21:29Z","updated":"2024-02-21T03:21:29Z","authors":["Weile Luo","Ruibo Fan","Zeyu Li","Dayou Du","Qiang Wang","Xiaowen Chu"],"categories":["cs.AR"],"summary":"Graphics processing units (GPUs) are continually evolving to cater to the computational demands of contemporary general-purpose workloads, particularly those driven by artificial intelligence (AI) utilizing deep learning techniques. A substantial body of studies have been dedicated to dissecting the microarchitectural metrics characterizing diverse GPU generations, which helps researchers understand the hardware details and leverage them to optimize the GPU programs. However, the latest Hopper GPUs present a set of novel attributes, including new tensor cores supporting FP8, DPX, and distributed shared memory. Their details still remain mysterious in terms of performance and operational characteristics. In this research, we propose an extensive benchmarking study focused on the Hopper GPU.","suggestedLayer":"paper","snapshotGeneratedAt":"2026-07-04T15:25:15.984Z"},"createdAt":"2026-07-05T07:10:14.445Z"},{"id":"candidate_entity_arxiv_paper_2411_10873","entityId":"paper_arxiv_2411_10873","type":"paper","name":"Developer Challenges on Large Language Models: A Study of Stack Overflow and OpenAI Developer Forum Posts","layer":"ai_infra","description":"arXiv paper candidate related to OpenAI; published 2024-11-16T19:38:27Z.","websiteUrl":"https://arxiv.org/abs/2411.10873v2","country":"","statusText":"preprint","valuation":"N/A","aliases":["Developer Challenges on Large Language Models: A Study of Stack Overflow and OpenAI Developer Forum Posts","2411.10873","cs.SE"],"confidence":0.6,"evidenceUrl":"https://arxiv.org/abs/2411.10873v2","extractionMethod":"arxiv_paper_snapshot","status":"candidate","payload":{"note":"arXiv paper candidate; review relevance, title, authors and topic categories before approving as a paper/research artifact entity.","sourceId":"src_arxiv","associatedEntityId":"openai","associatedEntityName":"OpenAI","arxivPaperId":"2411.10873","arxivUrl":"https://arxiv.org/abs/2411.10873v2","pdfUrl":"https://arxiv.org/pdf/2411.10873v2","normalizedArxivPaperId":"2411.10873","normalizedTitleKey":"developer challenges on large language models a study of stack overflow and openai developer forum posts","dedupKeys":["arxiv:2411.10873","title:developer challenges on large language models a study of stack overflow and openai developer forum posts","source:arxiv:title:developer challenges on large language models a study of stack overflow and openai developer forum posts"],"published":"2024-11-16T19:38:27Z","updated":"2024-11-22T22:24:58Z","authors":["Khairul Alam","Kartik Mittal","Banani Roy","Chanchal Roy"],"categories":["cs.SE"],"summary":"Large Language Models (LLMs) have gained widespread popularity due to their exceptional capabilities across various domains, including chatbots, healthcare, education, content generation, and automated support systems. However, developers encounter numerous challenges when implementing, fine-tuning, and integrating these models into real-world applications. This study investigates LLM developers' challenges by analyzing community interactions on Stack Overflow and OpenAI Developer Forum, employing BERTopic modeling to identify and categorize developer discussions. Our analysis yields nine challenges on Stack Overflow (e.g., LLM Ecosystem and Challenges, API Usage, LLM Training with Frameworks) and 17 on the OpenAI Developer Forum (e.g., API Usage and Error Handling, Fine-Tuning and Dataset","suggestedLayer":"paper","snapshotGeneratedAt":"2026-07-04T15:25:15.984Z"},"createdAt":"2026-07-05T07:10:14.444Z"},{"id":"candidate_entity_arxiv_paper_2502_06807","entityId":"paper_arxiv_2502_06807","type":"paper","name":"Competitive Programming with Large Reasoning Models","layer":"ai_infra","description":"arXiv paper candidate related to OpenAI; published 2025-02-03T23:00:15Z.","websiteUrl":"https://arxiv.org/abs/2502.06807v2","country":"","statusText":"preprint","valuation":"N/A","aliases":["Competitive Programming with Large Reasoning Models","2502.06807","cs.LG","cs.AI","cs.CL"],"confidence":0.6,"evidenceUrl":"https://arxiv.org/abs/2502.06807v2","extractionMethod":"arxiv_paper_snapshot","status":"candidate","payload":{"note":"arXiv paper candidate; review relevance, title, authors and topic categories before approving as a paper/research artifact entity.","sourceId":"src_arxiv","associatedEntityId":"openai","associatedEntityName":"OpenAI","arxivPaperId":"2502.06807","arxivUrl":"https://arxiv.org/abs/2502.06807v2","pdfUrl":"https://arxiv.org/pdf/2502.06807v2","normalizedArxivPaperId":"2502.06807","normalizedTitleKey":"competitive programming with large reasoning models","dedupKeys":["arxiv:2502.06807","title:competitive programming with large reasoning models","source:arxiv:title:competitive programming with large reasoning models"],"published":"2025-02-03T23:00:15Z","updated":"2025-02-18T22:21:40Z","authors":["OpenAI","Ahmed El-Kishky","Alexander Wei","Andre Saraiva","Borys Minaiev","Daniel Selsam","David Dohan","Francis Song","Hunter Lightman","Ignasi Clavera","Jakub Pachocki"],"categories":["cs.LG","cs.AI","cs.CL"],"summary":"We show that reinforcement learning applied to large language models (LLMs) significantly boosts performance on complex coding and reasoning tasks. Additionally, we compare two general-purpose reasoning models - OpenAI o1 and an early checkpoint of o3 - with a domain-specific system, o1-ioi, which uses hand-engineered inference strategies designed for competing in the 2024 International Olympiad in Informatics (IOI). We competed live at IOI 2024 with o1-ioi and, using hand-crafted test-time strategies, placed in the 49th percentile. Under relaxed competition constraints, o1-ioi achieved a gold medal. However, when evaluating later models such as o3, we find that o3 achieves gold without hand-crafted domain-specific strategies or relaxed constraints. Our findings show that although speciali","suggestedLayer":"paper","snapshotGeneratedAt":"2026-07-04T15:25:15.984Z"},"createdAt":"2026-07-05T07:10:14.444Z"},{"id":"candidate_entity_arxiv_paper_2502_10303","entityId":"paper_arxiv_2502_10303","type":"paper","name":"Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations","layer":"ai_infra","description":"arXiv paper candidate related to Google DeepMind; published 2025-02-14T17:06:34Z.","websiteUrl":"https://arxiv.org/abs/2502.10303v2","country":"","statusText":"preprint","valuation":"N/A","aliases":["Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations","2502.10303","cs.AI","cs.GT"],"confidence":0.6,"evidenceUrl":"https://arxiv.org/abs/2502.10303v2","extractionMethod":"arxiv_paper_snapshot","status":"candidate","payload":{"note":"arXiv paper candidate; review relevance, title, authors and topic categories before approving as a paper/research artifact entity.","sourceId":"src_arxiv","associatedEntityId":"deepmind","associatedEntityName":"Google DeepMind","arxivPaperId":"2502.10303","arxivUrl":"https://arxiv.org/abs/2502.10303v2","pdfUrl":"https://arxiv.org/pdf/2502.10303v2","normalizedArxivPaperId":"2502.10303","normalizedTitleKey":"reinforcement learning in strategy based and atari games a review of google deepminds innovations","dedupKeys":["arxiv:2502.10303","title:reinforcement learning in strategy based and atari games a review of google deepminds innovations","source:arxiv:title:reinforcement learning in strategy based and atari games a review of google deepminds innovations"],"published":"2025-02-14T17:06:34Z","updated":"2026-02-11T09:17:09Z","authors":["Abdelrhman Shaheen","Anas Badr","Ali Abohendy","Hatem Alsaadawy","Nadine Alsayad","Ehab H. El-Shazly"],"categories":["cs.AI","cs.GT"],"summary":"Reinforcement Learning (RL) has been widely used in many applications, particularly in gaming, which serves as an excellent training ground for AI models. Google DeepMind has pioneered innovations in this field, employing reinforcement learning algorithms, including model-based, model-free, and deep Q-network approaches, to create advanced AI models such as AlphaGo, AlphaGo Zero, and MuZero. AlphaGo, the initial model, integrates supervised learning and reinforcement learning to master the game of Go, surpassing professional human players. AlphaGo Zero refines this approach by eliminating reliance on human gameplay data, instead utilizing self-play for enhanced learning efficiency. MuZero further extends these advancements by learning the underlying dynamics of game environments without ex","suggestedLayer":"paper","snapshotGeneratedAt":"2026-07-04T15:25:15.984Z"},"createdAt":"2026-07-05T07:10:14.445Z"},{"id":"candidate_entity_arxiv_paper_2503_11698","entityId":"paper_arxiv_2503_11698","type":"paper","name":"A Comparison of the Cerebras Wafer-Scale Integration Technology with Nvidia GPU-based Systems for Artificial Intelligence","layer":"ai_infra","description":"arXiv paper candidate related to NVIDIA; published 2025-03-11T22:57:42Z.","websiteUrl":"https://arxiv.org/abs/2503.11698v1","country":"","statusText":"preprint","valuation":"N/A","aliases":["A Comparison of the Cerebras Wafer-Scale Integration Technology with Nvidia GPU-based Systems for Artificial Intelligence","2503.11698","cs.AR"],"confidence":0.6,"evidenceUrl":"https://arxiv.org/abs/2503.11698v1","extractionMethod":"arxiv_paper_snapshot","status":"candidate","payload":{"note":"arXiv paper candidate; review relevance, title, authors and topic categories before approving as a paper/research artifact entity.","sourceId":"src_arxiv","associatedEntityId":"nvidia","associatedEntityName":"NVIDIA","arxivPaperId":"2503.11698","arxivUrl":"https://arxiv.org/abs/2503.11698v1","pdfUrl":"https://arxiv.org/pdf/2503.11698v1","normalizedArxivPaperId":"2503.11698","normalizedTitleKey":"a comparison of the cerebras wafer scale integration technology with nvidia gpu based systems for artificial intelligence","dedupKeys":["arxiv:2503.11698","title:a comparison of the cerebras wafer scale integration technology with nvidia gpu based systems for artificial intelligence","source:arxiv:title:a comparison of the cerebras wafer scale integration technology with nvidia gpu based systems for artificial intelligence"],"published":"2025-03-11T22:57:42Z","updated":"2025-03-11T22:57:42Z","authors":["Yudhishthira Kundu","Manroop Kaur","Tripty Wig","Kriti Kumar","Pushpanjali Kumari","Vivek Puri","Manish Arora"],"categories":["cs.AR"],"summary":"Cerebras' wafer-scale engine (WSE) technology merges multiple dies on a single wafer. It addresses the challenges of memory bandwidth, latency, and scalability, making it suitable for artificial intelligence. This work evaluates the WSE-3 architecture and compares it with leading GPU-based AI accelerators, notably Nvidia's H100 and B200. The work highlights the advantages of WSE-3 in performance per watt and memory scalability and provides insights into the challenges in manufacturing, thermal management, and reliability. The results suggest that wafer-scale integration can surpass conventional architectures in several metrics, though work is required to address cost-effectiveness and long-term viability.","suggestedLayer":"paper","snapshotGeneratedAt":"2026-07-04T15:25:15.984Z"},"createdAt":"2026-07-05T07:10:14.445Z"},{"id":"candidate_entity_arxiv_paper_2503_20481","entityId":"paper_arxiv_2503_20481","type":"paper","name":"Analyzing Modern NVIDIA GPU cores","layer":"ai_infra","description":"arXiv paper candidate related to NVIDIA; published 2025-03-26T12:10:53Z.","websiteUrl":"https://arxiv.org/abs/2503.20481v1","country":"","statusText":"preprint","valuation":"N/A","aliases":["Analyzing Modern NVIDIA GPU cores","2503.20481","cs.AR"],"confidence":0.6,"evidenceUrl":"https://arxiv.org/abs/2503.20481v1","extractionMethod":"arxiv_paper_snapshot","status":"candidate","payload":{"note":"arXiv paper candidate; review relevance, title, authors and topic categories before approving as a paper/research artifact entity.","sourceId":"src_arxiv","associatedEntityId":"nvidia","associatedEntityName":"NVIDIA","arxivPaperId":"2503.20481","arxivUrl":"https://arxiv.org/abs/2503.20481v1","pdfUrl":"https://arxiv.org/pdf/2503.20481v1","normalizedArxivPaperId":"2503.20481","normalizedTitleKey":"analyzing modern nvidia gpu cores","dedupKeys":["arxiv:2503.20481","title:analyzing modern nvidia gpu cores","source:arxiv:title:analyzing modern nvidia gpu cores"],"published":"2025-03-26T12:10:53Z","updated":"2025-03-26T12:10:53Z","authors":["Rodrigo Huerta","Mojtaba Abaie Shoushtary","José-Lorenzo Cruz","Antonio González"],"categories":["cs.AR"],"summary":"GPUs are the most popular platform for accelerating HPC workloads, such as artificial intelligence and science simulations. However, most microarchitectural research in academia relies on GPU core pipeline designs based on architectures that are more than 15 years old. This paper reverse engineers modern NVIDIA GPU cores, unveiling many key aspects of its design and explaining how GPUs leverage hardware-compiler techniques where the compiler guides hardware during execution. In particular, it reveals how the issue logic works including the policy of the issue scheduler, the structure of the register file and its associated cache, and multiple features of the memory pipeline. Moreover, it analyses how a simple instruction prefetcher based on a stream buffer fits well with modern NVIDIA GPUs","suggestedLayer":"paper","snapshotGeneratedAt":"2026-07-04T15:25:15.984Z"},"createdAt":"2026-07-05T07:10:14.445Z"},{"id":"candidate_entity_arxiv_paper_2508_14444","entityId":"paper_arxiv_2508_14444","type":"paper","name":"NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model","layer":"ai_infra","description":"arXiv paper candidate related to NVIDIA; published 2025-08-20T06:00:57Z.","websiteUrl":"https://arxiv.org/abs/2508.14444v4","country":"","statusText":"preprint","valuation":"N/A","aliases":["NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model","2508.14444","cs.CL","cs.AI","cs.LG"],"confidence":0.6,"evidenceUrl":"https://arxiv.org/abs/2508.14444v4","extractionMethod":"arxiv_paper_snapshot","status":"candidate","payload":{"note":"arXiv paper candidate; review relevance, title, authors and topic categories before approving as a paper/research artifact entity.","sourceId":"src_arxiv","associatedEntityId":"nvidia","associatedEntityName":"NVIDIA","arxivPaperId":"2508.14444","arxivUrl":"https://arxiv.org/abs/2508.14444v4","pdfUrl":"https://arxiv.org/pdf/2508.14444v4","normalizedArxivPaperId":"2508.14444","normalizedTitleKey":"nvidia nemotron nano 2 an accurate and efficient hybrid mamba transformer reasoning model","dedupKeys":["arxiv:2508.14444","title:nvidia nemotron nano 2 an accurate and efficient hybrid mamba transformer reasoning model","source:arxiv:title:nvidia nemotron nano 2 an accurate and efficient hybrid mamba transformer reasoning model"],"published":"2025-08-20T06:00:57Z","updated":"2025-09-02T16:12:36Z","authors":["NVIDIA","Aarti Basant","Abhijit Khairnar","Abhijit Paithankar","Abhinav Khattar","Adithya Renduchintala","Aditya Malte","Akhiad Bercovich","Akshay Hazare","Alejandra Rico","Aleksander Ficek"],"categories":["cs.CL","cs.AI","cs.LG"],"summary":"We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compared to similarly-sized models. Nemotron-Nano-9B-v2 builds on the Nemotron-H architecture, in which the majority of the self-attention layers in the common Transformer architecture are replaced with Mamba-2 layers, to achieve improved inference speed when generating the long thinking traces needed for reasoning. We create Nemotron-Nano-9B-v2 by first pre-training a 12-billion-parameter model (Nemotron-Nano-12B-v2-Base) on 20 trillion tokens using an FP8 training recipe. 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