--- title: "Other — papers, page 14" description: "Other: 743 papers of the Elamur Research library · page 14 of 30 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/other/page/14/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/other/page/14/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Other](https://md.research.elamur.ai/free/en/shelves/other/) / page 14 # 📦 Other 743 papers · page 14 of 30 · newest first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Works whose topics fit none of the fields above yet. [← prev](https://md.research.elamur.ai/free/en/shelves/other/page/13/) [1](https://md.research.elamur.ai/free/en/shelves/other/) … [13](https://md.research.elamur.ai/free/en/shelves/other/page/13/) **14** [15](https://md.research.elamur.ai/free/en/shelves/other/page/15/) … [30](https://md.research.elamur.ai/free/en/shelves/other/page/30/) [next →](https://md.research.elamur.ai/free/en/shelves/other/page/15/) - [Knowledge Unlearning for Mitigating Privacy Risks in Language Models](https://md.research.elamur.ai/free/en/papers/jang-2022-knowledge-unlearning-mitigating-privacy-risks/) — Joel Jang, Dongkeun Yoon, Sohee Yang, et al. · 2022 - [LAION-5B: An open large-scale dataset for training next generation image-text models](https://md.research.elamur.ai/free/en/papers/schuhmann-2022-laion-5b-open-large-scale/) — Christoph Schuhmann, Romain Beaumont, Richard Vencu, et al. · 2022 - [LaMDA: Language Models for Dialog Applications](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/) — Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al. · 2022 - [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://md.research.elamur.ai/free/en/papers/saparov-2022-language-models-greedy-reasoners-systematic/) — Abulhair Saparov, He He · 2022 - [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://md.research.elamur.ai/free/en/papers/huang-2022-language-models-zero-shot-planners/) — Wenlong Huang, Pieter Abbeel, Deepak Pathak, et al. · 2022 - [Large Language Models Are Human-Level Prompt Engineers](https://md.research.elamur.ai/free/en/papers/zhou-2022-large-language-models-human-level/) — Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, et al. · 2022 - [Large Language Models Encode Clinical Knowledge](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/) — Karan Singhal, Shekoofeh Azizi, Tao Tu, et al. · 2022 - [Large Language Models Struggle to Learn Long-Tail Knowledge](https://md.research.elamur.ai/free/en/papers/kandpal-2022-large-language-models-struggle-learn/) — Nikhil Kandpal, Haikang Deng, Adam Roberts, et al. · 2022 - [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://md.research.elamur.ai/free/en/papers/huang-2022-layoutlmv3-pre-training-document-ai/) — Yupan Huang, Tengchao Lv, Lei Cui, et al. · 2022 - [Leveraging Large Language Models for Multiple Choice Question Answering](https://md.research.elamur.ai/free/en/papers/robinson-2022-leveraging-large-language-models-multiple/) — Joshua Robinson, Christopher Michael Rytting, David Wingate · 2022 - [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://md.research.elamur.ai/free/en/papers/song-2022-llm-planner-few-shot-grounded/) — Chan Hee Song, Jiaman Wu, Clayton Washington, et al. · 2022 - [MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://md.research.elamur.ai/free/en/papers/pal-2022-medmcqa-large-scale-multi-subject/) — Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu · 2022 - [Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/) — Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al. · 2022 - [mGPT: Few-Shot Learners Go Multilingual](https://md.research.elamur.ai/free/en/papers/shliazhko-2022-mgpt-few-shot-learners-go/) — Oleh Shliazhko, Alena Fenogenova, Maria Tikhonova, et al. · 2022 - [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/) — Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al. · 2022 - [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://md.research.elamur.ai/free/en/papers/wang-2022-ofa-unifying-architectures-tasks-modalities/) — Peng Wang, An Yang, Rui Men, et al. · 2022 - [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://md.research.elamur.ai/free/en/papers/su-2022-one-embedder-any-task-instruction/) — Hongjin Su, Weijia Shi, Jungo Kasai, et al. · 2022 - [OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization](https://md.research.elamur.ai/free/en/papers/iyer-2022-opt-iml-scaling-language-model/) — Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al. · 2022 - [Out of One, Many: Using Language Models to Simulate Human Samples](https://md.research.elamur.ai/free/en/papers/argyle-2022-out-one-many-using-language/) — Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, et al. · 2022 - [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://md.research.elamur.ai/free/en/papers/chen-2022-pali-jointly-scaled-multilingual-language/) — Xi Chen, Xiao Wang, Soravit Changpinyo, et al. · 2022 - [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://md.research.elamur.ai/free/en/papers/saharia-2022-photorealistic-text-image-diffusion-models/) — Chitwan Saharia, William Chan, Saurabh Saxena, et al. · 2022 - [PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://md.research.elamur.ai/free/en/papers/valmeekam-2022-planbench-extensible-benchmark-evaluating-large/) — Karthik Valmeekam, Matthew Marquez, Alberto Olmo, et al. · 2022 - [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://md.research.elamur.ai/free/en/papers/gao-2022-precise-zero-shot-dense-retrieval/) — Luyu Gao, Xueguang Ma, Jimmy Lin, et al. · 2022 - [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://md.research.elamur.ai/free/en/papers/singh-2022-progprompt-generating-situated-robot-task/) — Ishika Singh, Valts Blukis, Arsalan Mousavian, et al. · 2022 - [Promptagator: Few-shot Dense Retrieval From 8 Examples](https://md.research.elamur.ai/free/en/papers/dai-2022-promptagator-few-shot-dense-retrieval/) — Zhuyun Dai, Vincent Y. Zhao, Ji Ma, et al. · 2022 [← prev](https://md.research.elamur.ai/free/en/shelves/other/page/13/) [1](https://md.research.elamur.ai/free/en/shelves/other/) … [13](https://md.research.elamur.ai/free/en/shelves/other/page/13/) **14** [15](https://md.research.elamur.ai/free/en/shelves/other/page/15/) … [30](https://md.research.elamur.ai/free/en/shelves/other/page/30/) [next →](https://md.research.elamur.ai/free/en/shelves/other/page/15/)