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Achieving Peak Performance for Large Language Models: A Systematic Review | 2,024 | [["Zhyar Rzgar K Rostam", "Buda Health Center"], ["S\u00e1ndor Sz\u00e9n\u00e1si", "Buda Health Center"], ["G\u00e1bor Kert\u00e9sz", "Institute for Computer Science and Control"]] | IEEE Access | In recent years, large language models (LLMs) have achieved remarkable\nsuccess in natural language processing (NLP). LLMs require an extreme amount of\nparameters to attain high performance. As models grow into the\ntrillion-parameter range, computational and memory costs increase\nsignificantly. This makes it difficu... | true | 4 | The abstract addresses algorithmic efficiency through training optimization and pruning, architectural design via LLM architecture trends, and hardware optimization with accelerator-aware methods. While it lacks focus on data processing selection, it provides a comprehensive review of methods relevant to making AI more... | {"algorithmic_efficiency": "Discusses training optimization and pruning strategies", "architectural_design": "Reviews architectural approaches in LLMs for efficiency", "data_processing_selection": "Does not address data selection or preprocessing", "hardware_optimization": "Explicitly covers hardware optimization strat... | {"references": [], "citations": ["achieving_peak_performance_for_large_language_models_a_systematic_review"]} | {"embedding": [0.018744714558124542, -0.020137012004852295, -0.0032274711411446333, 0.0011716699227690697, 0.023248694837093353, 0.008666163310408592, -0.01151387020945549, -0.02878744900226593, 0.02361520566046238, -0.010858137160539627, -0.04070904105901718, -0.002274261089041829, -0.007021588739007711, -0.0526145286... | {"umap_x": 2.6593964099884033, "umap_y": 4.380886077880859} | achieving_peak_performance_for_large_language_models_a_systematic_review | null | |
MizAR 60 for Mizar 50 | 2,023 | [["Ashish Vaswani", ""], ["Noam Shazeer", ""], ["Niki Parmar", ""], ["Jakob Uszkoreit", ""], ["Llion Jones", ""], ["Aidan N. Gomez", ""], ["\u0141ukasz Kaiser", ""], ["Illia Polosukhin", ""]] | Leibniz-Zentrum für Informatik (Schloss Dagstuhl) | As a present to Mizar on its 50th anniversary, we develop an AI/TP system that automatically proves about 60% of the Mizar theorems in the hammer setting. We also automatically prove 75% of the Mizar theorems when the automated provers are helped by using only the premises used in the human-written Mizar proofs. We des... | false | 3 | The paper addresses algorithmic efficiency through learning-based premise selection and data processing via active learning and corpus growth. However, it does not discuss architectural design, hardware co-design, or explicit optimization for deployment or training efficiency. While it touches on efficiency in automate... | {"algorithmic_efficiency": "Learning-based premise selection improves proof efficiency", "architectural_design": "No novel architecture proposed; relies on existing provers", "data_processing_selection": "Active learning and premise selection for proof generation", "hardware_optimization": "Not addressed; focuses on so... | {"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review", "dfx_a_lowlatency_multifpga_appliance_for_accelerating_transformerbased_text_generation", "lightseq2_accelerated_training_for_transformerbased_models_on_gpus", "easy_and_efficient_transformer_scalable_inference_solution_for_larg... | {"embedding": [0.007636173628270626, 0.029408464208245277, -0.016905926167964935, 0.02631826512515545, 0.013454781845211983, 0.01152308750897646, -0.022448567673563957, -0.018709110096096992, -0.03171120956540108, 0.023483285680413246, -0.009818196296691895, -0.022903451696038246, 0.001448972150683403, -0.0005680182948... | {"umap_x": 0.9614489078521729, "umap_y": 1.2693313360214233} | mizar_60_for_mizar_50 | null | |
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey | 2,023 | [["Bonan Min", "Amazon (United States)"], ["Hayley Ross", "Harvard University Press"], ["Elior Sulem", "California University of Pennsylvania"], ["Amir Pouran Ben Veyseh", "University of Oregon"], ["Thien Huu Nguyen", "University of Oregon"], ["Oscar Sainz", "University of the Basque Country"], ["Eneko Agirre", "Univer... | ACM Computing Surveys | Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. For numerous NLP tasks, approaches leveraging PLMs have achieved state-of-the-art performance. The key idea is to learn a generic, latent representation of language from a generic task on... | false | 2 | The abstract focuses on PLM architectures and training paradigms but does not discuss algorithmic efficiency, architectural optimizations for speed/memory, data reduction methods, or hardware co-design. It provides a general survey of NLP advancements rather than addressing efficiency or accessibility for trainers or d... | {"algorithmic_efficiency": "Not addressed", "architectural_design": "Not addressed", "data_processing_selection": "Not addressed", "hardware_optimization": "Not addressed"} | {"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review", "a_survey_of_text_classification_with_transformers_how_wide_how_large_how_long_how_accurate_how_expensive_how_safe"], "citations": ["easy_and_efficient_transformer_scalable_inference_solution_for_large_nlp_model"]} | {"embedding": [0.013605968095362186, 0.0028972828295081854, -0.007290709298104048, 0.027001887559890747, 0.000169931270647794, 0.008308188989758492, -0.00027251054416410625, -0.05947703495621681, -0.009720870293676853, 0.002297352533787489, -0.006747138220816851, -0.000958088377956301, 0.0088969049975276, -0.0156478676... | {"umap_x": 0.470488041639328, "umap_y": 2.85550856590271} | recent_advances_in_natural_language_processing_via_large_pretrained_language_models_a_survey | null | |
Talking about Large Language Models | 2,024 | [["Murray Shanahan", "Imperial College London"]] | Communications of the ACM | Interacting with a contemporary LLM-based conversational agent can create an illusion of being in the presence of a thinking creature. Yet, in their very nature, such systems are fundamentally not like us. | false | 1 | The abstract discusses the phenomenological aspect of LLM interactions but does not touch on algorithmic efficiency, architectural design, data processing, or hardware optimization. It lacks technical details relevant to making AI more efficient or accessible for trainers and deployers. | {"algorithmic_efficiency": "not addressed", "architectural_design": "not addressed", "data_processing_selection": "not addressed", "hardware_optimization": "not addressed"} | {"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review"], "citations": []} | {"embedding": [0.018812954425811768, 0.025025248527526855, -0.017862729728221893, 0.009612304158508778, 0.02036122977733612, 0.024820098653435707, -0.0011499490356072783, -0.05679279565811157, -0.010810595005750656, 0.0258053969591856, -0.0251341313123703, -0.009443783201277256, -0.007301933132112026, -0.00335749844089... | {"umap_x": -0.6382922530174255, "umap_y": 2.345301389694214} | talking_about_large_language_models | https://dl.acm.org/doi/pdf/10.1145/3624724 | |
DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale | 2,022 | [["Reza Yazdani Aminabadi", "Microsoft (United States)"], ["Samyam Rajbhandari", "Microsoft (United States)"], ["Ammar Ahmad Awan", "Microsoft (United States)"], ["Cheng Li", "Microsoft (United States)"], ["Canbing Li", "Microsoft (United States)"], ["Elton Zheng", "Microsoft (United States)"], ["Olatunji Ruwase", "Mic... | The landscape of transformer model inference is increasingly diverse in model size, model characteristics, latency and throughput requirements, hardware requirements, etc. With such diversity, designing a versatile inference system is challenging. DeepSpeed-Inference addresses these challenges by (1) a multi-GPU infere... | true | 4 | The abstract addresses algorithmic efficiency through sparse transformers and memory optimization, architectural design via multi-GPU and heterogeneous systems, and hardware optimization through CPU/NVMe/GPU co-design. It directly supports the research question by advancing efficient, scalable inference for large model... | {"algorithmic_efficiency": "Focuses on sparse transformers and memory-efficient inference", "architectural_design": "Provides multi-GPU and heterogeneous architecture for scalable inference", "data_processing_selection": "NONE", "hardware_optimization": "Leverages CPU/NVMe/GPU co-design for hardware-aware inference"} | {"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review", "efficient_llms_training_and_inference_an_introduction", "norm_tweaking_highperformance_lowbit_quantization_of_large_language_models"], "citations": ["deepspeed_inference_enabling_efficient_inference_of_transformer_models_at_unp... | {"embedding": [0.005358639173209667, 0.012594535015523434, -0.014781453646719456, 0.0105776097625494, 0.01512176264077425, -0.00659312354400754, 0.014560402370989323, -0.020127423107624054, -0.005209506954997778, 0.0002483512507751584, 0.007294585928320885, -0.01147187314927578, -0.027572590857744217, 0.000366264575859... | {"umap_x": 3.242175579071045, "umap_y": 5.023370742797852} | deepspeed_inference_enabling_efficient_inference_of_transformer_models_at_unprecedented_scale | null | ||
Language Model Behavior: A Comprehensive Survey | 2,023 | [["Tyler A. Chang", "University of California, San Diego"], ["Benjamin Bergen", "University of California, San Diego"]] | Computational Linguistics | Abstract Transformer language models have received widespread public attention, yet their generated text is often surprising even to NLP researchers. In this survey, we discuss over 250 recent studies of English language model behavior before task-specific fine-tuning. Language models possess basic capabilities in synt... | false | 1 | The abstract does not discuss algorithmic efficiency, architectural design, data processing/selection, or hardware optimization. It focuses broadly on language model behavior, capabilities, and limitations rather than efficiency or accessibility improvements for trainers and deployers. | {"algorithmic_efficiency": "not addressed", "architectural_design": "not addressed", "data_processing_selection": "not addressed", "hardware_optimization": "not addressed"} | {"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review"], "citations": ["easy_and_efficient_transformer_scalable_inference_solution_for_large_nlp_model"]} | {"embedding": [0.013850832358002663, 0.009598132222890854, -0.022173628211021423, 0.026053739711642265, 0.018428746610879898, 0.02667960338294506, -0.005014568567276001, -0.025276267901062965, -0.019177529960870743, -0.0012178962351754308, -0.021060362458229065, 0.003723887959495187, 0.016096685081720352, -0.0233343504... | {"umap_x": 0.15042969584465027, "umap_y": 2.475184202194214} | language_model_behavior_a_comprehensive_survey | https://direct.mit.edu/coli/article-pdf/doi/10.1162/coli_a_00492/2177312/coli_a_00492.pdf | |
Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training | 2,023 | [["Shenggui Li", "Singapore Institute of Technology"], ["Hongxin Liu", ""], ["Zhengda Bian", ""], ["Jiarui Fang", ""], ["Haichen Huang", ""], ["Yuliang Liu", ""], ["Boxiang Wang", "Singapore Institute of Technology"], ["Yang You", "National University of Singapore"]] | The success of Transformer models has pushed the deep learning model scale to billions of parameters, but the memory limitation of a single GPU has led to an urgent need for training on multi-GPU clusters. However, the best practice for choosing the optimal parallel strategy is still lacking, as it requires domain expe... | false | 3 | The abstract does not focus on algorithmic efficiency, architectural design, or data processing/selective strategies. It addresses hardware scalability through parallelism in distributed training, indirectly relating to hardware optimization via multi-GPU and heterogeneous system support. While relevant to large-scale ... | {"algorithmic_efficiency": "Not addressed", "architectural_design": "Not directly addressed", "data_processing_selection": "Not addressed", "hardware_optimization": "Indirectly considered via hardware-aware parallelism"} | {"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review", "efficient_llms_training_and_inference_an_introduction"], "citations": []} | {"embedding": [-0.010109851136803627, 0.03784191980957985, -0.0004919135826639831, 0.0004271979269105941, -0.0022992815356701612, 0.01298289094120264, -0.004713696893304586, -0.03154461830854416, 0.007398037705570459, 0.021724022924900055, -0.022841591387987137, 0.005944942589849234, 0.0027237236499786377, -0.011469012... | {"umap_x": 3.6683053970336914, "umap_y": 4.17811918258667} | colossalai_a_unified_deep_learning_system_for_largescale_parallel_training | null | ||
ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs | 2,023 | [["Yujia Zhai", "University of California, Riverside"], ["Chengquan Jiang", ""], ["Leyuan Wang", ""], ["Xiaoying Jia", ""], ["Shang Zhang", "Nvidia (United States)"], ["Zizhong Chen", "University of California, Riverside"], ["Xin Liu", ""], ["Yibo Zhu", ""]] | Transformers have become keystone models in natural language processing over the past decade. They have achieved great popularity in deep learning applications, but the increasing sizes of the parameter spaces required by transformer models generate a commensurate need to accelerate performance. Natural language proces... | false | 4 | The paper addresses algorithmic efficiency through a padding-free approach that eliminates redundant computations on padded tokens, and enhances architectural design via optimized Multi-Head Attention modules. These improvements directly reduce computational and memory overhead in variable-length sequence processing, a... | {"algorithmic_efficiency": "Padding-free computation reduces redundant operations", "architectural_design": "Architecture-aware MHA optimizations improve performance", "data_processing_selection": "NONE", "hardware_optimization": "NONE"} | {"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review"], "citations": []} | {"embedding": [0.00898724514991045, 0.011069969274103642, -0.026894288137555122, 0.02090602181851864, 0.023065006360411644, -0.009173288941383362, 0.02364027500152588, -0.021254217252135277, -0.002448089886456728, -0.01972067542374134, -0.013147646561264992, -0.01075529120862484, 0.0014350530691444874, -0.0182675495743... | {"umap_x": 1.7971622943878174, "umap_y": 4.869078636169434} | bytetransformer_a_highperformance_transformer_boosted_for_variablelength_inputs | null | ||
Sentiment Analysis with Neural Models for Hungarian | 2,023 | [["L\u00e1szl\u00f3 J\u00e1nos Laki", "Hungarian Research Centre for Linguistics"], ["Zijian Gy\u0151z\u0151 Yang", ""]] | Acta Polytechnica Hungarica | Sentiment analysis is a powerful tool to gain insight into the emotional polarity of opinionated texts.Computerized applications can contribute to the establishment of nextgeneration models that can provide us with data of unprecedented quantity and quality.However, these models often require substantial amount of reso... | false | 3 | The paper addresses algorithmic efficiency through data augmentation to reduce training resource needs and improves model performance. While it uses advanced architectural designs (transformers) and data processing (cross-lingual transfer), it does not discuss hardware co-design or optimization. It is relevant to AI ef... | {"algorithmic_efficiency": "Data augmentation improves model efficiency", "architectural_design": "Uses neural transformer architecture for Hungarian sentiment analysis", "data_processing_selection": "Applies machine translation and cross-lingual transfer to expand training data", "hardware_optimization": "NONE"} | {"references": ["achieving_peak_performance_for_large_language_models_a_systematic_review"], "citations": []} | {"embedding": [-0.016728023067116737, 0.0011090640909969807, -0.005491161718964577, -0.023904835805296898, 0.03722536936402321, 0.00013329229841474444, 0.006434789393097162, -0.05593295022845268, 0.02333408035337925, 0.0019759542774409056, -0.02092576026916504, 0.0033093139063566923, -0.022312456741929054, -0.034247461... | {"umap_x": -0.4204283058643341, "umap_y": 4.553020000457764} | sentiment_analysis_with_neural_models_for_hungarian | https://doi.org/10.12700/aph.20.5.2023.5.8 | |
ResearchRabbit (product review) | 2,023 | [["Victoria Cole", "University of Ottawa"], ["Mish Boutet", "University of Ottawa"]] | "Journal of the Canadian Health Libraries Association / Journal de l Association de bilbiothèques d(...TRUNCATED) | "ResearchRabbit is a scholarly publication discovery tool supported by artificial intelligence (AI).(...TRUNCATED) | false | 1 | "The abstract describes a scholarly discovery tool that uses AI for recommendation generation based (...TRUNCATED) | "{\"algorithmic_efficiency\": \"Not applicable\", \"architectural_design\": \"Not applicable\", \"da(...TRUNCATED) | "{\"references\": [\"achieving_peak_performance_for_large_language_models_a_systematic_review\"], \"(...TRUNCATED) | "{\"embedding\": [-0.024287216365337372, 0.010205757804214954, -0.012635764665901661, -0.02005367912(...TRUNCATED) | {"umap_x": 0.004384573083370924, "umap_y": 3.5795931816101074} | researchrabbit_product_review | https://journals.library.ualberta.ca/jchla/index.php/jchla/article/download/29699/21872 |
End of preview. Expand in Data Studio
ai_efficiency_selection
About This Dataset
This dataset was created using 🏖️ Tidepool Research: LLM-Enabled Literature Review, an interactive tool for building comprehensive literature review corpora through systematic discovery, evaluation, and organization of academic papers.
The app uses language models to score paper relevance and citation networks to discover related work. You can use the app to create your own literature review datasets or explore this corpus interactively.
🔗 Try the app: https://huggingface.co/spaces/hfmlsoc/Lit_Review_with_LMs
Research Question
Papers on making artificial intelligence more efficient and accessible to trainers and deployers, taking into account algorithms, architecture, data processing and selection, and the role of hardware and hardware optimization
Configuration
- Academic API: OpenAlex
- Cutoff Year: 2022
- Fetch Mode: References + Citations
- LLM Model: Qwen/Qwen3-4B-Instruct-2507
- LLM Mode: HF Inference Endpoint
- Endpoint: qwen3-next-80b-a3b-instruct-tpx
Research Aspects
- algorithmic_efficiency: Focus on compression, pruning, quantization, or lightweight models to reduce computational cost during training/inference. Examples: knowledge distillation, sparse networks, low-precision training.
- architectural_design: Novel model architectures optimized for speed, memory, or energy without sacrificing performance. Examples: mobile-optimized CNNs, transformer variants like TinyBERT, modular designs.
- data_processing_selection: Strategies to reduce data burden via selection, synthesis, or preprocessing. Examples: active learning, data distillation, synthetic data generation, efficient labeling pipelines.
- hardware_optimization: Co-design of software with hardware (e.g., accelerators, edge devices) for efficiency. Examples: FPGA/TPU-aware models, on-device inference frameworks, power-aware scheduling.
Dataset Information
This dataset contains papers collected and analyzed for a literature review. Papers are scored for relevance to the research question using LLM-based analysis.
Note: Dataset metadata (research question, configuration, etc.) is stored in metadata.json for efficient browsing without downloading the full dataset.
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