Просмотр кода страницы Большая языковая модель

== Примечания ==

=== Комментарии ===
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=== Источники ===
{{примечания|refs=
<ref name=Manning-2022>{{cite journal
|last=Manning
|first=Christopher D.
|author-link=Christopher D. Manning
|title=Human Language Understanding & Reasoning
|journal=Daedalus
|year=2022
|url=https://www.amacad.org/publication/human-language-understanding-reasoning
|access-date=2023-03-16
|archive-date=2023-11-17
|archive-url=https://web.archive.org/web/20231117205531/https://www.amacad.org/publication/human-language-understanding-reasoning
|url-status=live
}}</ref>
<ref name=extracting>{{Cite conference |last1=Carlini |first1=Nicholas |last2=Tramer |first2=Florian |last3=Wallace |first3=Eric |last4=Jagielski |first4=Matthew |last5=Herbert-Voss |first5=Ariel |last6=Lee |first6=Katherine |last7=Roberts |first7=Adam |last8=Brown |first8=Tom B |last9=Song |first9=Dawn |last10=Erlingsson |first10=Ulfar |title=Extracting Training Data from Large Language Models |conference=USENIX Security Symposium |volume=6 |year=2021 |url=https://www.usenix.org/system/files/sec21-carlini-extracting.pdf |access-date=2023-03-16 |archive-date=2023-12-21 |archive-url=https://web.archive.org/web/20231221210608/https://www.usenix.org/system/files/sec21-carlini-extracting.pdf |url-status=live }}</ref>
<ref name=emergent>{{cite web
|title=Emergent Abilities of Large Language Models
|last1=Wei
|first1=Jason
|url=https://arxiv.org/abs/2206.07682
|access-date=2023-03-16
|archive-date=2023-03-16
|archive-url=https://web.archive.org/web/20230316140558/https://arxiv.org/abs/2206.07682
|deadlink=no
}}</ref>
<ref name=jm>{{cite book |last1=Jurafsky |first1=Dan |last2=Martin |first2=James H. |title=Speech and Language Processing |date=2023-01-07|edition=3rd edition draft |url=https://web.stanford.edu/~jurafsky/slp3/ed3book_jan72023.pdf |access-date=2022-05-24 }} {{Wayback|url=https://web.stanford.edu/~jurafsky/slp3/ed3book_jan72023.pdf |date=20230323210221 }}</ref>
<ref name=Wiggers>{{cite web
|work=TechCrunch
|last=Wiggers
|first=Kyle
|date=2022-04-28
|title=The emerging types of language models and why they matter
|url=https://techcrunch.com/2022/04/28/the-emerging-types-of-language-models-and-why-they-matter/
|access-date=2023-03-16
|archive-date=2023-03-16
|archive-url=https://web.archive.org/web/20230316072443/https://techcrunch.com/2022/04/28/the-emerging-types-of-language-models-and-why-they-matter/
|deadlink=no
}}</ref>
<ref name=bigger-better>{{cite web
|work=Nature
|last=Ananthaswamy
|first=Anil
|title=In AI, is bigger always better?
|date=2023-03-08
|url=https://www.nature.com/articles/d41586-023-00641-w
|access-date=2023-03-16
|archive-date=2023-03-16
|archive-url=https://web.archive.org/web/20230316181013/https://www.nature.com/articles/d41586-023-00641-w
|deadlink=no
}}</ref>
<ref name=kaplan-scaling>{{Cite journal |last1=Kaplan |first1=Jared |last2=McCandlish |first2=Sam |last3=Henighan |first3=Tom |last4=Brown |first4=Tom B. |last5=Chess |first5=Benjamin |last6=Child |first6=Rewon |last7=Gray |first7=Scott |last8=Radford |first8=Alec |last9=Wu |first9=Jeffrey |last10=Amodei |first10=Dario |title=Scaling Laws for Neural Language Models |journal=CoRR |volume=abs/2001.08361 |year=2020 |url=https://arxiv.org/abs/2001.08361 |eprint=2001.08361 |access-date=2023-03-16 |archive-date=2023-03-15 |archive-url=https://web.archive.org/web/20230315213534/https://arxiv.org/abs/2001.08361 |url-status=live }}</ref>
<ref name="few-shot-learners">{{cite journal|first1=Tom B.|last1=Brown|first2=Benjamin|last2=Mann|last3=Ryder|last25=Chess|last20=Hesse|first20=Christopher|last21=Chen|first21=Mark|last22=Sigler|first22=Eric|last23=Litwin|first23=Mateusz|last24=Gray|first24=Scott|first26=Jack|first25=Benjamin|last26=Clark|last19=Winter|last27=Berner|first27=Christopher|last28=McCandlish|first28=Sam|last29=Radford|first29=Alec|last30=Sutskever|first30=Ilya|last31=Amodei|first31=Dario|first19=Clemens|first18=Jeffrey|first3=Nick|last10=Askell|last4=Subbiah|first4=Melanie|last5=Kaplan|first5=Jared|last6=Dhariwal|first6=Prafulla|last7=Neelakantan|first7=Arvind|last8=Shyam|first8=Pranav|last9=Sastry|first9=Girish|first10=Amanda|last18=Wu|last11=Agarwal|first11=Sandhini|last12=Herbert-Voss|first12=Ariel|last13=Krueger|first13=Gretchen|last14=Henighan|first14=Tom|last15=Child|first15=Rewon|last16=Ramesh|first16=Aditya|last17=Ziegler|first17=Daniel M.|editor1-last=Larochelle|editor1-first=H.|editor2-last=Ranzato|editor2-first=M.|editor3-last=Hadsell|editor3-first=R.|editor4-last=Balcan|editor4-first=M.F.|editor5-last=Lin|editor5-first=H.|title=Language Models are Few-Shot Learners|journal=Advances in Neural Information Processing Systems|date=Dec 2020|volume=33|pages=1877–1901|url=https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf|publisher=Curran Associates, Inc.|access-date=2023-03-16|archive-date=2023-11-17|archive-url=https://web.archive.org/web/20231117204007/https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf|url-status=live}}</ref>

<!-- Refs below are specific to the "List of large language models" section. (Keeping separate in case that section is split off into a standalone list article in the future.) -->
<ref name=glam-blog>{{Cite web |last1=Dai |first1=Andrew M |last2=Du |first2=Nan |date=2021-12-09 |title=More Efficient In-Context Learning with GLaM |url=https://ai.googleblog.com/2021/12/more-efficient-in-context-learning-with.html |access-date=2023-03-09 |website=ai.googleblog.com |language=en |archive-date=2023-03-12 |archive-url=https://web.archive.org/web/20230312072042/https://ai.googleblog.com/2021/12/more-efficient-in-context-learning-with.html |deadlink=no }}</ref>
<ref name=lamda-blog>{{Cite web |last1=Cheng |first1=Heng-Tze |last2=Thoppilan |first2=Romal |date=2022-01-21 |title=LaMDA: Towards Safe, Grounded, and High-Quality Dialog Models for Everything |url=https://ai.googleblog.com/2022/01/lamda-towards-safe-grounded-and-high.html |access-date=2023-03-09 |website=ai.googleblog.com |language=en |archive-date=2022-03-25 |archive-url=https://web.archive.org/web/20220325014118/https://ai.googleblog.com/2022/01/lamda-towards-safe-grounded-and-high.html |deadlink=no }}</ref>
<ref name=palm-blog>{{Cite web |last1=Narang |first1=Sharan |last2=Chowdhery |first2=Aakanksha |date=2022-04-04 |title=Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance |url=https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html |access-date=2023-03-09 |website=ai.googleblog.com |language=en |archive-date=2022-04-04 |archive-url=https://web.archive.org/web/20220404161447/https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html |deadlink=no }}</ref>
<ref name=mtnlg-preprint><templatestyles src="Module:Citation/CS1/styles.css"></templatestyles><cite id="CITEREFSmithPatwaryNorickLeGresley2022" class="citation arxiv cs1">Smith, Shaden; Patwary, Mostofa; Norick, Brandon; LeGresley, Patrick; Rajbhandari, Samyam; Casper, Jared; Liu, Zhun; Prabhumoye, Shrimai; Zerveas, George; Korthikanti, Vijay; Zhang, Elton; Child, Rewon; Aminabadi, Reza Yazdani; Bernauer, Julie; Song, Xia (2022-02-04). "Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model". [[arXiv]]:<span class="cs1-lock-free" title="Freely accessible">[https://arxiv.org/abs/2201.11990 2201.11990] {{Wayback|url=https://arxiv.org/abs/2201.11990 |date=20230415083726 }}</span></cite></ref>
<ref name=bert-paper>{{cite arXiv |last1=Devlin |first1=Jacob |last2=Chang |first2=Ming-Wei |last3=Lee |first3=Kenton |last4=Toutanova |first4=Kristina |title=BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding |date=2018-10-11 |eprint=1810.04805v2|class=cs.CL }}</ref>
<ref name="15Brelease">{{Cite web
 |url          = https://openai.com/blog/gpt-2-1-5b-release/
 |title        = GPT-2: 1.5B Release
 |date         = 2019-11-05
 |website      = OpenAI
 |language     = en
 |access-date  = 2019-11-14
 |archive-date = 2019-11-14
 |archive-url  = https://web.archive.org/web/20191114074358/https://openai.com/blog/gpt-2-1-5b-release/
 |url-status   = live
}}</ref>
<ref name=chatgpt-blog>{{Cite web |date=2022-11-30 |title=ChatGPT: Optimizing Language Models for Dialogue |url=https://openai.com/blog/chatgpt/ |access-date=2023-01-13 |website=OpenAI |language=en |archive-date=2022-11-30 |archive-url=https://web.archive.org/web/20221130180912/https://openai.com/blog/chatgpt/ |deadlink=no }}</ref>
<ref name=vb-gpt-neo>{{cite web
|work=VentureBeat
|last=Iyer
|first=Abhishek
|title=GPT-3’s free alternative GPT-Neo is something to be excited about
|date=2021-05-15
|url=https://venturebeat.com/ai/gpt-3s-free-alternative-gpt-neo-is-something-to-be-excited-about/
|access-date=2023-03-16
|archive-date=2023-03-09
|archive-url=https://web.archive.org/web/20230309012717/https://venturebeat.com/ai/gpt-3s-free-alternative-gpt-neo-is-something-to-be-excited-about/
|deadlink=no
}}</ref>
<ref name=chinchilla-blog>{{cite web
|work=Deepmind Blog
|title=An empirical analysis of compute-optimal large language model training
|first1=Jordan
|last1=Hoffmann
|first2=Sebastian
|last2=Borgeaud
|first3=Arthur
|last3=Mensch
|first4=Laurent
|last4=Sifre
|date=2022-04-12
|url=https://www.deepmind.com/blog/an-empirical-analysis-of-compute-optimal-large-language-model-training
|access-date=2023-03-16
|archive-date=2022-04-13
|archive-url=https://web.archive.org/web/20220413014510/https://www.deepmind.com/blog/an-empirical-analysis-of-compute-optimal-large-language-model-training
|deadlink=no
}}</ref>
<ref name=llama-blog>{{cite web
|work=Meta AI
|title=Introducing LLaMA: A foundational, 65-billion-parameter large language model
|date=2023-02-24
|url=https://ai.facebook.com/blog/large-language-model-llama-meta-ai/
|access-date=2023-03-16
|archive-date=2023-03-03
|archive-url=https://web.archive.org/web/20230303112302/https://ai.facebook.com/blog/large-language-model-llama-meta-ai/
|deadlink=no
}}</ref>
}}

[[Категория:Обработка естественного языка]]
[[Категория:Глубокое обучение]]
[[Категория:Большие языковые модели| ]]