--- title: "Computing & GPUs — papers, page 2" description: "Computing & GPUs: 121 papers of the Elamur Research library · page 2 of 5 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/computing/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/computing/page/2/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Computing & GPUs](https://md.research.elamur.ai/free/en/shelves/computing/) / page 2 # 🖥️ Computing & GPUs 121 papers · page 2 of 5 · newest first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) GPUs and parallel computing, compilers and languages, systems, databases, security, search and the web. [← prev](https://md.research.elamur.ai/free/en/shelves/computing/) [1](https://md.research.elamur.ai/free/en/shelves/computing/) **2** [3](https://md.research.elamur.ai/free/en/shelves/computing/page/3/) [4](https://md.research.elamur.ai/free/en/shelves/computing/page/4/) [5](https://md.research.elamur.ai/free/en/shelves/computing/page/5/) [next →](https://md.research.elamur.ai/free/en/shelves/computing/page/3/) - [GlotLID: Language Identification for Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/kargaran-2023-glotlid-language-identification-low-resource/) — Amir Hossein Kargaran, Ayyoob Imani, François Yvon, et al. · 2023 - [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://md.research.elamur.ai/free/en/papers/liu-2023-your-code-generated-chatgpt-really/) — Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, et al. · 2023 - [Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations](https://md.research.elamur.ai/free/en/papers/inan-2023-llama-guard-llm-based-input/) — Hakan Inan, Kartikeya Upasani, Jianfeng Chi, et al. · 2023 - [Mathematical Discoveries from Program Search with Large Language Models](https://md.research.elamur.ai/free/en/papers/paredes-2023-mathematical-discoveries-program-search-large/) — Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, et al. · 2023 - [MIRACL: A Multilingual Retrieval Dataset Covering 18 Diverse Languages](https://md.research.elamur.ai/free/en/papers/zhang-2023-miracl-multilingual-retrieval-dataset-covering/) — Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, et al. · 2023 - [Query2doc: Query Expansion with Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2023-query2doc-query-expansion-large-language/) — Liang Wang, Nan Yang, Furu Wei · 2023 - [Reflexion: Language Agents with Verbal Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/shinn-2023-reflexion-language-agents-verbal-reinforcement/) — Noah Shinn, Federico Cassano, Edward Berman, et al. · 2023 - [Ring Attention with Blockwise Transformers for Near-Infinite Context](https://md.research.elamur.ai/free/en/papers/liu-2023-ring-attention-blockwise-transformers-near/) — Hao Liu, Matei Zaharia, Pieter Abbeel · 2023 - [SGLang: Efficient Execution of Structured Language Model Programs](https://md.research.elamur.ai/free/en/papers/zheng-2023-sglang-efficient-execution-structured-language/) — Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, et al. · 2023 - [SqueezeLLM: Dense-and-Sparse Quantization](https://md.research.elamur.ai/free/en/papers/kim-2023-squeezellm-dense-sparse-quantization/) — Sehoon Kim, Coleman Hooper, Amir Gholami, et al. · 2023 - [A Holistic Approach to Undesired Content Detection in the Real World](https://md.research.elamur.ai/free/en/papers/markov-2022-holistic-approach-undesired-content-detection/) — Todor Markov, Chong Zhang, Sandhini Agarwal, et al. · 2022 - [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://md.research.elamur.ai/free/en/papers/grootendorst-2022-bertopic-neural-topic-modeling-class/) — Maarten Grootendorst · 2022 - [CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/le-2022-coderl-mastering-code-generation-through/) — Hung Le, Yue Wang, Akhilesh Deepak Gotmare, et al. · 2022 - [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/) — Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al. · 2022 - [Competition-Level Code Generation with AlphaCode](https://md.research.elamur.ai/free/en/papers/li-2022-competition-level-code-generation-alphacode/) — Yujia Li, David Choi, Junyoung Chung, et al. · 2022 - [DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale](https://md.research.elamur.ai/free/en/papers/rajbhandari-2022-deepspeed-moe-advancing-mixture-experts/) — Samyam Rajbhandari, Conglong Li, Zhewei Yao, et al. · 2022 - [Efficient Training of Language Models to Fill in the Middle](https://md.research.elamur.ai/free/en/papers/bavarian-2022-efficient-training-language-models-fill/) — Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, et al. · 2022 - [MTEB: Massive Text Embedding Benchmark](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-mteb-massive-text-embedding-benchmark/) — Niklas Muennighoff, Nouamane Tazi, Loïc Magne, et al. · 2022 - [Reducing Activation Recomputation in Large Transformer Models](https://md.research.elamur.ai/free/en/papers/korthikanti-2022-reducing-activation-recomputation-large-transformer/) — Vijay Korthikanti, Jared Casper, Sangkug Lym, et al. · 2022 - [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://md.research.elamur.ai/free/en/papers/guo-2022-unixcoder-unified-cross-modal-pre/) — Daya Guo, Shuai Lu, Nan Duan, et al. · 2022 - [ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://md.research.elamur.ai/free/en/papers/yao-2022-zeroquant-efficient-affordable-post-training/) — Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al. · 2022 - [8-bit Optimizers via Block-wise Quantization](https://md.research.elamur.ai/free/en/papers/dettmers-2021-8-bit-optimizers-block-wise/) — Tim Dettmers, Mike Lewis, Sam Shleifer, et al. · 2021 - [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://md.research.elamur.ai/free/en/papers/thakur-2021-beir-heterogenous-benchmark-zero/) — Nandan Thakur, Nils Reimers, Andreas Rücklé, et al. · 2021 - [COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining](https://md.research.elamur.ai/free/en/papers/meng-2021-coco-lm-correcting-contrasting-text/) — Yu Meng, Chenyan Xiong, Payal Bajaj, et al. · 2021 - [Evaluating Large Language Models Trained on Code](https://md.research.elamur.ai/free/en/papers/chen-2021-evaluating-large-language-models-trained/) — Mark Chen, Jerry Tworek, Heewoo Jun, et al. · 2021 [← prev](https://md.research.elamur.ai/free/en/shelves/computing/) [1](https://md.research.elamur.ai/free/en/shelves/computing/) **2** [3](https://md.research.elamur.ai/free/en/shelves/computing/page/3/) [4](https://md.research.elamur.ai/free/en/shelves/computing/page/4/) [5](https://md.research.elamur.ai/free/en/shelves/computing/page/5/) [next →](https://md.research.elamur.ai/free/en/shelves/computing/page/3/)