Results 11 to 20 of about 22,635,901 (285)

Language change for the worse [PDF]

Many theories hold that language change, at least on a local level, is driven by a need for improvement. The present volume explores to what extent this assumption holds true, and whether there is a particular type of language change that we dub language
Schaden, Gerhard, Hyman, Larry M.; University of California, Berkeley, Elsweiler, Christine, Hölzl, Andreas, Faytak, Matthew, Hölzl, Andreas; University of Potsdam, Lapierre, Myriam, Weber, Thilo; Leibniz Institute for the German Language, Seiler, Guido; University of Zurich, Matoshi, Veton, Enke, Dankmar W.; LMU Munich, Nichols, Johanna; University of California, Berkeley, Deo, Ashwini, Huber, Judith, Reiner, Tabea, Jäger, Gerhard, Mühlenbernd, Roland   +16 more
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Use large language model to enhance reasoning of another large language model through reward updated GRPO [PDF]

Scientific Reports
Recent advancements in deep learning have significantly transformed natural language processing (NLP), enabling sophisticated reasoning and text generation.
Yiqiao Yin
doaj   +2 more sources

Large Language Models

Communications of the ACM, 2023
Large Language ModelsIn the latest edition of Stats, STAT!, Fralick and colleagues explain the statistics behind large language models - used in chat bots like ChatGPT and Bard. While these new tools may seem remarkably intelligent, at their core they just assemble sentences based on statistics from large amounts of text.
Michael, Fralick, Chana A, Sacks, Daniel, Muller, Tim, Vining, Emily, Ling, Jeffrey M, Drazen, C Corey, Hardin   +6 more
  +10 more sources

The Language Essence of the World: A Linguistic Interpretation of the Large Language Model

Computer Sciences & Mathematics Forum, 2023
The source of characters is hieroglyph. Hieroglyph is the imitation and reference of the phenomena and objects in the natural world. The objects and phenomena in the natural world are the original image.
Leiming Shi, Peng Wu
doaj   +1 more source

Autoformalization with Large Language Models

Advances in Neural Information Processing Systems 35, 2022
Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could advance the fields of formal verification, program synthesis, and artificial intelligence.
Wu, Yuhuai, Jiang, Qiaochu, Li, Wenda, Rabe, Markus, Staats, Charles, Jamnik, Mateja, Szegedy, Christian   +6 more
openaire   +5 more sources

Risk Analysis and Response Strategies of Large Language Models for Security Governance [PDF]

中国工程科学
To address the challenges of fragmented understanding of Large Language Model (LLM) security risks and the inadequacy of LLM risk classification and grading frameworks, this study aims to construct a comprehensive framework that integrates risk mechanism
Kun Jia, Yuxin Zhang, Jiyun Chen, Jiayin Qi, Binxing Fang   +4 more
doaj   +1 more source

Igbo-English intrasentential codeswitching and the Matrix Language Frame model [PDF]

, 2015
This paper uses data from Igbo-English intrasentential codeswitching involving mixed nominal expressions to test the Matrix Language Frame (MLF) model.
Fagundes Camargos, Quesler; Federal University of Rondônia, Nicolle, Steve; Canada Institute of Linguistics / SIL, Muaka, Leonard; Winston Salem State University, North Carolina, Kawasha, Boniface; Savannah State University, Jerro, Kyle; University of Texas at Austin, Anderson, Gregory D.S.; Living Tongues Institute for Endangered Languages & University of South Africa (UNISA), van der Wal, Jenneke; University of Cambridge, Ngonyani, Deo; Michigan State University, Bowler, Margit; UCLA, Ahland, Michael; California State University, Long Beach, Kari, Ethelbert E.; University of Botswana / University of Port Harcourt, Michieka, Martha; East Tennessee State University, McPherson, Laura; Dartmouth College, Namyalo, Saudah; Makerere University, Korsah, Sampson; Universität Leipzig, Aly, Ann M.; University of California, Los Angeles, Glewwe, Eleanor; University of California, Los Angeles, Pacchiarotti, Sara; University of Oregon, Bosire, Mokaya; University of Oregon, Sibanda, Galen; Michigan State University, Bonfim Duarte, Fábio; Federal University of Minas Gerais, Landman, Meredith; Pomona College, Ihemere, Kelechukwu; University of Westminster, Rolle, Nicholas; University of California, Berkeley, Osam, E. Kweku; University of Ghana, Sarvasy, Hannah; Australian National University, Ihemere, K., Teo, Amos; University of Oregon, Burkholder, Ross; University of Chicago, Gluckmann, John; UCLA, Nilsson, Morgan; University of Gothenburg, Ngowa, Nancy Jumwa; Pwani University, Cahill, Michael; SIL, Safir, Ken; Rutgers, Payne, Doris L.; University of Oregon, Schwarz, Anne; Universidad Regional Amazónica IKIAM, Ahland, Colleen; SIL, Selvanathan, Naga; Rutgers, Atelela Ngunga, Armindoo Saúl; University of Eduardo Mondlane, Moodie, Jonathan; University of Melbourne   +39 more
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Developing a language support model for mainstream primary school teachers [PDF]

, 2010
In the UK, speech and language therapists (SLTs) work with teachers to support children with language impairment (LI) in mainstream schools. Consultancy approaches are often used, where SLTs advise educational staff who then deliver language-learning ...
Ellis, Sue, Mary Turnbull, Elspeth McCartney, Kerr, J., McCartney, Elspeth, Boyle, James, Turnbull, Mary, Kerr, Jane, Sue Ellis, Jane Kerr, James Boyle   +10 more
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A Watermark for Large Language Models

CoRR, 2023
Potential harms of large language models can be mitigated by watermarking model output, i.e., embedding signals into generated text that are invisible to humans but algorithmically detectable from a short span of tokens. We propose a watermarking framework for proprietary language models.
John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, Tom Goldstein   +5 more
openaire   +3 more sources

Survey of Model Compression for Large Language Model [PDF]

Jisuanji kexue yu tansuo
Large language models (LLMs) have attracted considerable attention in recent years due to their strong cognitive capabilities and widespread applications in various fields. However, their tremendous demand for computation and memory makes it difficult to
GUO Jinyang, HE Changyi, YANG Ge, LIU Xianglong
doaj   +1 more source