Inżynieria podpowiedzi (Polish Wikipedia)

Xiang LisaX.L. Li Xiang LisaX.L., PercyP. Liang PercyP., Prefix-Tuning: Optimizing Continuous Prompts for Generation, „Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)”, Online: Association for Computational Linguistics, 2021, s. 4582–4597, DOI: 10.18653/v1/2021.acl-long.353 [dostęp 2023-03-24] (ang.).
BrianB. Lester BrianB., RamiR. Al-Rfou RamiR., NoahN. Constant NoahN., The Power of Scale for Parameter-Efficient Prompt Tuning, „Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing”, Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, 2021, s. 3045–3059, DOI: 10.18653/v1/2021.emnlp-main.243 [dostęp 2023-03-24] (ang.).

anthropic.com

Many-shot jailbreaking [online], www.anthropic.com [dostęp 2024-04-09] (ang.).

arthur.ai

Arthur Shield [online], www.arthur.ai [dostęp 2024-03-20] (ang.).

arxiv.org

PengfeiP. Liu PengfeiP. i inni, Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing, „ArXiv”, 2021, DOI: 10.48550/arxiv.2107.13586, arXiv:2107.13586 .
JasonJ. Wei JasonJ. i inni, Emergent Abilities of Large Language Models, „arXiv”, 2022, DOI: 10.48550/ARXIV.2206.07682, arXiv:2206.07682 .
Xiang LisaX.L. Li Xiang LisaX.L., PercyP. Liang PercyP., Prefix-Tuning: Optimizing Continuous Prompts for Generation, „arXiv”, 2021, DOI: 10.48550/ARXIV.2101.00190, arXiv:2101.00190 .
Tom B.T.B. Brown Tom B.T.B. i inni, Language Models are Few-Shot Learners, „arXiv”, 2020, DOI: 10.48550/ARXIV.2005.14165, arXiv:2005.14165 .
PranabP. Sahoo PranabP. i inni, A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications, „Arxiv”, 2024, DOI: 10.48550/ARXIV.2402.07927, arXiv:2402.07927 .
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?, „arXiv”, arXiv:2202.12837 (ang.).
TakeshiT. Kojima TakeshiT. i inni, Large Language Models are Zero-Shot Reasoners, „arXiv”, 2022, DOI: 10.48550/ARXIV.2205.11916, arXiv:2205.11916 .
https://arxiv.org/pdf/2309.03409.pdf
Scaling Instruction-Finetuned Language Models, „arXiv”, arXiv:2210.11416 (ang.).
DennyD. Zhou DennyD. i inni, Least-to-Most Prompting Enables Complex Reasoning in Large Language Models, „Arxiv”, 2022, DOI: 10.48550/ARXIV.2205.10625, arXiv:2205.10625 .
XuezhiX. Wang XuezhiX. i inni, Self-Consistency Improves Chain of Thought Reasoning in Language Models, „Arxiv”, 2022, DOI: 10.48550/ARXIV.2203.11171, arXiv:2203.11171 .
ShizheS. Diao ShizheS. i inni, Active Prompting with Chain-of-Thought for Large Language Models, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2302.12246, arXiv:2302.12246 .
JieyiJ. Long JieyiJ., Large Language Model Guided Tree-of-Thought, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2305.08291, arXiv:2305.08291 .
ShunyuS. Yao ShunyuS. i inni, Tree of Thoughts: Deliberate Problem Solving with Large Language Models, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2305.10601, arXiv:2305.10601 .
XufengX. Zhao XufengX. i inni, Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic, „arXiv”, 2023, DOI: 10.48550/ARXIV.2309.13339, arXiv:2309.13339 .
MononitoM. Goswami MononitoM. i inni, AQuA: A Benchmarking Tool for Label Quality Assessment, „arXiv”, 2023, DOI: 10.48550/ARXIV.2306.09467, arXiv:2306.09467 .
YaoY. Yao YaoY., ZuchaoZ. Li ZuchaoZ., HaiH. Zhao HaiH., Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2305.16582, arXiv:2305.16582 .
JaehunJ. Jung JaehunJ. i inni, Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations, „Arxiv”, 2022, DOI: 10.48550/ARXIV.2205.11822, arXiv:2205.11822 .
ZekunZ. Li ZekunZ. i inni, Guiding Large Language Models via Directional Stimulus Prompting, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2302.11520, arXiv:2302.11520 .
ChengCh. Li ChengCh. i inni, Large Language Models Understand and Can be Enhanced by Emotional Stimuli, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2307.11760, arXiv:2307.11760 .
Emotional Manipulation Through Prompt Engineering Amplifies Disinformation Generation in AI Large Language Models, „arXiv”, arXiv:2403.03550 (ang.).
HanxuH. Hu HanxuH. i inni, Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models, „arXiv”, 2023, DOI: 10.48550/ARXIV.2305.10276, arXiv:2305.10276 .
YuchengY. Zhou YuchengY. i inni, Thread of Thought Unraveling Chaotic Contexts, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2311.08734, arXiv:2311.08734 .
ZilongZ. Wang ZilongZ. i inni, Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding, „Arxiv”, 2024, DOI: 10.48550/ARXIV.2401.04398, arXiv:2401.04398 .
VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models, „arXiv”, arXiv:2403.06098 (ang.).
ChrisanthaCh. Fernando ChrisanthaCh. i inni, Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2309.16797, arXiv:2309.16797 .
ReidR. Pryzant ReidR. i inni, Automatic Prompt Optimization with "Gradient Descent" and Beam Search, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2305.03495, arXiv:2305.03495 .
QingyanQ. Guo QingyanQ. i inni, Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2309.08532, arXiv:2309.08532 .
YongchaoY. Zhou YongchaoY. i inni, Large Language Models Are Human-Level Prompt Engineers, „Arxiv”, 2022, DOI: 10.48550/ARXIV.2211.01910, arXiv:2211.01910 .
PenghaoP. Zhao PenghaoP. i inni, Retrieval-Augmented Generation for AI-Generated Content: A Survey, „Arxiv”, 2024, DOI: 10.48550/ARXIV.2402.19473, arXiv:2402.19473 .
YunfanY. Gao YunfanY. i inni, Retrieval-Augmented Generation for Large Language Models: A Survey, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2312.10997, arXiv:2312.10997 .
ZhuoshiZ. Pan ZhuoshiZ. i inni, LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression, „Arxiv”, 2024, DOI: 10.48550/ARXIV.2403.12968, arXiv:2403.12968 .
Segment Anything, „arXiv”, arXiv:2304.02643 (ang.).
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion, „arXiv”, arXiv:2208.01618 (ang.).
WeikangW. Zhou WeikangW. i inni, EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models, „Arxiv”, 2024, DOI: 10.48550/ARXIV.2403.12171, arXiv:2403.12171 .
Ignore Previous Prompt: Attack Techniques For Language Models, „arXiv”, arXiv:2211.09527 (ang.).
Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples, „arXiv”, arXiv:2209.02128 (ang.).

cnet.com

Google's Latest AI Model Can Be Taught How to Solve Problems [online], CNET [dostęp 2024-03-18] (ang.).

coursera.org

How to Become a Prompt Engineer: Skills You Need + Steps to Take [online], Coursera, 19 marca 2024 [dostęp 2024-03-21] (pol.).

cureus.com

HussamH. Alkaissi HussamH., Samy IS.I. McFarlane Samy IS.I., Artificial Hallucinations in ChatGPT: Implications in Scientific Writing, „Cureus”, 2023, DOI: 10.7759/cureus.35179, ISSN 2168-8184, PMID: 36811129, PMCID: PMC9939079 [dostęp 2023-03-24] (ang.).

doi.org

dx.doi.org

PengfeiP. Liu PengfeiP. i inni, Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing, „ArXiv”, 2021, DOI: 10.48550/arxiv.2107.13586, arXiv:2107.13586 .
HussamH. Alkaissi HussamH., Samy IS.I. McFarlane Samy IS.I., Artificial Hallucinations in ChatGPT: Implications in Scientific Writing, „Cureus”, 2023, DOI: 10.7759/cureus.35179, ISSN 2168-8184, PMID: 36811129, PMCID: PMC9939079 [dostęp 2023-03-24] (ang.).
JasonJ. Wei JasonJ. i inni, Emergent Abilities of Large Language Models, „arXiv”, 2022, DOI: 10.48550/ARXIV.2206.07682, arXiv:2206.07682 .
Xiang LisaX.L. Li Xiang LisaX.L., PercyP. Liang PercyP., Prefix-Tuning: Optimizing Continuous Prompts for Generation, „Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)”, Online: Association for Computational Linguistics, 2021, s. 4582–4597, DOI: 10.18653/v1/2021.acl-long.353 [dostęp 2023-03-24] (ang.).
BrianB. Lester BrianB., RamiR. Al-Rfou RamiR., NoahN. Constant NoahN., The Power of Scale for Parameter-Efficient Prompt Tuning, „Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing”, Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, 2021, s. 3045–3059, DOI: 10.18653/v1/2021.emnlp-main.243 [dostęp 2023-03-24] (ang.).
Xiang LisaX.L. Li Xiang LisaX.L., PercyP. Liang PercyP., Prefix-Tuning: Optimizing Continuous Prompts for Generation, „arXiv”, 2021, DOI: 10.48550/ARXIV.2101.00190, arXiv:2101.00190 .
Tom B.T.B. Brown Tom B.T.B. i inni, Language Models are Few-Shot Learners, „arXiv”, 2020, DOI: 10.48550/ARXIV.2005.14165, arXiv:2005.14165 .
PranabP. Sahoo PranabP. i inni, A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications, „Arxiv”, 2024, DOI: 10.48550/ARXIV.2402.07927, arXiv:2402.07927 .
TakeshiT. Kojima TakeshiT. i inni, Large Language Models are Zero-Shot Reasoners, „arXiv”, 2022, DOI: 10.48550/ARXIV.2205.11916, arXiv:2205.11916 .
DennyD. Zhou DennyD. i inni, Least-to-Most Prompting Enables Complex Reasoning in Large Language Models, „Arxiv”, 2022, DOI: 10.48550/ARXIV.2205.10625, arXiv:2205.10625 .
XuezhiX. Wang XuezhiX. i inni, Self-Consistency Improves Chain of Thought Reasoning in Language Models, „Arxiv”, 2022, DOI: 10.48550/ARXIV.2203.11171, arXiv:2203.11171 .
ShizheS. Diao ShizheS. i inni, Active Prompting with Chain-of-Thought for Large Language Models, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2302.12246, arXiv:2302.12246 .
JieyiJ. Long JieyiJ., Large Language Model Guided Tree-of-Thought, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2305.08291, arXiv:2305.08291 .
ShunyuS. Yao ShunyuS. i inni, Tree of Thoughts: Deliberate Problem Solving with Large Language Models, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2305.10601, arXiv:2305.10601 .
XufengX. Zhao XufengX. i inni, Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic, „arXiv”, 2023, DOI: 10.48550/ARXIV.2309.13339, arXiv:2309.13339 .
MononitoM. Goswami MononitoM. i inni, AQuA: A Benchmarking Tool for Label Quality Assessment, „arXiv”, 2023, DOI: 10.48550/ARXIV.2306.09467, arXiv:2306.09467 .
YaoY. Yao YaoY., ZuchaoZ. Li ZuchaoZ., HaiH. Zhao HaiH., Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Language Models, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2305.16582, arXiv:2305.16582 .
JaehunJ. Jung JaehunJ. i inni, Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations, „Arxiv”, 2022, DOI: 10.48550/ARXIV.2205.11822, arXiv:2205.11822 .
Edward Y.E.Y. Chang Edward Y.E.Y., Prompting Large Language Models With the Socratic Method, IEEE, 8 marca 2023, s. 0351–0360, DOI: 10.1109/CCWC57344.2023.10099179, ISBN 979-8-3503-3286-5 [dostęp 2024-03-24] .
ZekunZ. Li ZekunZ. i inni, Guiding Large Language Models via Directional Stimulus Prompting, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2302.11520, arXiv:2302.11520 .
ChengCh. Li ChengCh. i inni, Large Language Models Understand and Can be Enhanced by Emotional Stimuli, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2307.11760, arXiv:2307.11760 .
HanxuH. Hu HanxuH. i inni, Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models, „arXiv”, 2023, DOI: 10.48550/ARXIV.2305.10276, arXiv:2305.10276 .
YuchengY. Zhou YuchengY. i inni, Thread of Thought Unraveling Chaotic Contexts, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2311.08734, arXiv:2311.08734 .
ZilongZ. Wang ZilongZ. i inni, Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding, „Arxiv”, 2024, DOI: 10.48550/ARXIV.2401.04398, arXiv:2401.04398 .
ChrisanthaCh. Fernando ChrisanthaCh. i inni, Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2309.16797, arXiv:2309.16797 .
ReidR. Pryzant ReidR. i inni, Automatic Prompt Optimization with "Gradient Descent" and Beam Search, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2305.03495, arXiv:2305.03495 .
QingyanQ. Guo QingyanQ. i inni, Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2309.08532, arXiv:2309.08532 .
YongchaoY. Zhou YongchaoY. i inni, Large Language Models Are Human-Level Prompt Engineers, „Arxiv”, 2022, DOI: 10.48550/ARXIV.2211.01910, arXiv:2211.01910 .
PenghaoP. Zhao PenghaoP. i inni, Retrieval-Augmented Generation for AI-Generated Content: A Survey, „Arxiv”, 2024, DOI: 10.48550/ARXIV.2402.19473, arXiv:2402.19473 .
YunfanY. Gao YunfanY. i inni, Retrieval-Augmented Generation for Large Language Models: A Survey, „Arxiv”, 2023, DOI: 10.48550/ARXIV.2312.10997, arXiv:2312.10997 .
ZhuoshiZ. Pan ZhuoshiZ. i inni, LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression, „Arxiv”, 2024, DOI: 10.48550/ARXIV.2403.12968, arXiv:2403.12968 .
WeikangW. Zhou WeikangW. i inni, EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models, „Arxiv”, 2024, DOI: 10.48550/ARXIV.2403.12171, arXiv:2403.12171 .
MalgorzataM. Pikies MalgorzataM., JunadeJ. Ali JunadeJ., Analysis and safety engineering of fuzzy string matching algorithms, „ISA Transactions”, 113, 2021, s. 1–8, DOI: 10.1016/j.isatra.2020.10.014, ISSN 0019-0578 .

dorik.com

How to Write DALL-E Prompts? [online], dorik.com [dostęp 2024-03-13] .

forbes.com

SiladityaS. Ray SiladityaS., Samsung Bans ChatGPT Among Employees After Sensitive Code Leak [online], Forbes [dostęp 2024-03-20] (ang.).

github.com

XufengX. Zhao XufengX., xf-zhao/LoT [online], 2 kwietnia 2024 [dostęp 2024-04-03] .
ZekunZ. Li ZekunZ., Leezekun/Directional-Stimulus-Prompting [online], 17 marca 2024 [dostęp 2024-03-25] .
Hanxu, hanxuhu/chain-of-symbol-planning [online], 19 lutego 2024 [dostęp 2024-04-03] .
WenhaoW. Wang WenhaoW., WangWenhao0716/VidProM [online], 14 marca 2024 [dostęp 2024-03-14] .
KihoK. Lee KihoK., ChatGPT_DAN [online], luty 2023 [dostęp 2024-03-19] .
NVIDIA/NeMo-Guardrails, NVIDIA Corporation, 20 marca 2024 [dostęp 2024-03-20] .

google-research.github.io

MusicLM [online], google-research.github.io [dostęp 2024-03-14] .

hackaday.com

What’s Old Is New Again: GPT-3 Prompt Injection Attack Affects AI [online], Hackaday, 17 września 2022 [dostęp 2024-03-08] (ang.).

huggingface.co

lmsys/vicuna-33b-v1.3 · Hugging Face [online], huggingface.co [dostęp 2024-04-03] .

ieee.org

ieeexplore.ieee.org

Edward Y.E.Y. Chang Edward Y.E.Y., Prompting Large Language Models With the Socratic Method, IEEE, 8 marca 2023, s. 0351–0360, DOI: 10.1109/CCWC57344.2023.10099179, ISBN 979-8-3503-3286-5 [dostęp 2024-03-24] .

lakera.ai

The ELI5 Guide to Prompt Injection: Techniques, Prevention Methods & Tools | Lakera – Protecting AI teams that disrupt the world. [online], www.lakera.ai [dostęp 2024-03-20] .

lazarski.pl

lut.lazarski.pl

Czym jest prompt engineering?| LUT [online], Lazarski University of Technology [dostęp 2024-03-08] (pol.).

learnprompting.org

Learn Prompting: Your Guide to Communicating with AI [online], learnprompting.org [dostęp 2024-03-19] (ang.).
Learn Prompting: Your Guide to Communicating with AI [online], learnprompting.org [dostęp 2024-03-19] (ang.).
Learn Prompting: Your Guide to Communicating with AI [online], learnprompting.org [dostęp 2024-03-19] (ang.).

llmlingua.com

LLMLingua Series | Effectively Deliver Information to LLMs via Prompt Compression [online], llmlingua.com [dostęp 2024-04-13] .

medium.com

cobusgreyling.medium.com

CobusC. Greyling CobusC., Least To Most Prompting [online], Medium, 27 lipca 2023 [dostęp 2024-03-21] (ang.).

jimclydemonge.medium.com

Jim ClydeJ.C. Monge Jim ClydeJ.C., Dall-E2 VS Stable Diffusion: Same Prompt, Different Results [online], Medium, 25 sierpnia 2022 [dostęp 2024-03-13] (ang.).

meta.com

llama.meta.com

Llama [online], Llama [dostęp 2024-03-23] (ang.).

ai.meta.com

AudioCraft: A simple one-stop shop for audio modeling [online], ai.meta.com [dostęp 2024-03-14] (ang.).

microsoft.com

AlyssaA. Hughes AlyssaA., LLMLingua: Innovating LLM efficiency with prompt compression [online], Microsoft Research, 7 grudnia 2023 [dostęp 2024-03-16] (ang.).

money.pl

ŁukaszŁ. Czerwiński ŁukaszŁ., WojciechW. Pszonka WojciechW., Za chwilę posypią się oferty pracy dla prompt engineerów. Oto na czym polega nowy zawód [online], www.money.pl [dostęp 2023-04-12] (pol.).

nccgroup.com

research.nccgroup.com

Exploring Prompt Injection Attacks [online], NCC Group Research Blog, 5 grudnia 2022 [dostęp 2024-03-08] (ang.).

ncsc.gov.uk

Exercise caution when building off LLMs [online], www.ncsc.gov.uk [dostęp 2024-03-20] (ang.).

neurips.cc

proceedings.neurips.cc

JasonJ. Wei JasonJ. i inni, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, „Advances in Neural Information Processing Systems”, 35, 6 grudnia 2022, s. 24824–24837 [dostęp 2024-03-19] (ang.).
JasonJ. Wei JasonJ. i inni, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, „Advances in Neural Information Processing Systems”, 35, 6 grudnia 2022, s. 24824–24837 [dostęp 2024-03-19] (ang.).
JasonJ. Wei JasonJ. i inni, Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, „Advances in Neural Information Processing Systems”, 35, 6 grudnia 2022, s. 24824–24837 [dostęp 2024-03-19] (ang.).

nih.gov

ncbi.nlm.nih.gov

HussamH. Alkaissi HussamH., Samy IS.I. McFarlane Samy IS.I., Artificial Hallucinations in ChatGPT: Implications in Scientific Writing, „Cureus”, 2023, DOI: 10.7759/cureus.35179, ISSN 2168-8184, PMID: 36811129, PMCID: PMC9939079 [dostęp 2023-03-24] (ang.).

openai.com

Video generation models as world simulators [online], openai.com [dostęp 2024-03-14] (ang.).
GPT-4V(ision) system card [online], openai.com [dostęp 2024-03-20] (ang.).

cdn.openai.com

AlecA. Radford AlecA. i inni, Language Models are Unsupervised Multitask Learners [online], 1 stycznia 2019 [dostęp 2023-03-27] .

platform.openai.com

https://platform.openai.com/docs/guides/images/prompting

paperswithcode.com

Papers with Code - GSM8K Dataset [online], paperswithcode.com [dostęp 2024-04-03] (ang.).

praveengovindaraj.com

PraveenP. Govindaraj PraveenP., Thread of Thought (ThoT): A new Prompting approach to Complex Contexts [online], Medium, 19 listopada 2023 [dostęp 2024-04-04] (ang.).

rebuff.ai

rebuff.ai [online], www.rebuff.ai [dostęp 2024-03-20] .

reddit.com

Nin_kat, New jailbreak based on virtual functions - smuggle illegal tokens to the backend. [online], r/ChatGPT, 5 lutego 2023 [dostęp 2024-03-19] .

research.google

blog.research.google

Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance [online], blog.research.google, 4 kwietnia 2022 [dostęp 2024-03-18] (ang.).
Better Language Models Without Massive Compute [online], blog.research.google, 29 listopada 2022 [dostęp 2024-03-19] (ang.).
Chain-of-table: Evolving tables in the reasoning chain for table understanding [online], blog.research.google, 11 marca 2024 [dostęp 2024-04-04] (ang.).

searchenginejournal.com

RogerR. Montti RogerR., Google's Chain of Thought Prompting Can Boost Today's Best Algorithms [online], Search Engine Journal, 13 maja 2022 [dostęp 2024-03-18] (ang.).

segment-anything.com

Segment Anything | Meta AI [online], segment-anything.com [dostęp 2024-03-16] (ang.).

simonklug.de

Prompt Engineering 101 - Elevating Your Prompts to New Heights [online], simonklug.de [dostęp 2024-03-24] .

simonwillison.net

Prompt injection attacks against GPT-3 [online], simonwillison.net [dostęp 2024-03-08] (ang.).
Multi-modal prompt injection image attacks against GPT-4V [online], simonwillison.net [dostęp 2024-03-20] (ang.).

stanford.edu

aiindex.stanford.edu

https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report_2023.pdf

stockimg.ai

What is Negative Prompt and How to Use It [online], stockimg.ai, 11 marca 2024 [dostęp 2024-03-14] (ang.).

theregister.com

BrandonB. Vigliarolo BrandonB., GPT-3 'prompt injection' attack causes bot bad manners [online], www.theregister.com [dostęp 2024-03-08] (ang.).

venturebeat.com

So you want to be a prompt engineer: Critical careers of the future [online], VentureBeat, 17 września 2022 [dostęp 2023-03-23] (ang.).
EktaE. Dang EktaE., Harnessing the power of GPT-3 in scientific research [online], VentureBeat, 8 lutego 2023 [dostęp 2024-03-18] (ang.).
BenB. Dickson BenB., LLMs have not learned our language — we’re trying to learn theirs [online], VentureBeat, 30 sierpnia 2022 [dostęp 2024-03-19] (ang.).
Sean MichaelS.M. Kerner Sean MichaelS.M., Is it time to ‘shield’ AI with a firewall? Arthur AI thinks so [online], VentureBeat, 4 maja 2023 [dostęp 2024-03-20] (ang.).

vice.com

ChloeCh. Xiang ChloeCh., The Amateurs Jailbreaking GPT Say They're Preventing a Closed-Source AI Dystopia [online], Vice, 22 marca 2023 [dostęp 2024-03-19] (ang.).

wikidata.org

VictorV. Sanh VictorV. i inni, Multitask Prompted Training Enables Zero-Shot Task Generalization [online], 15 października 2021 [dostęp 2023-03-24] .

wired.com

MattM. Burgess MattM., The Security Hole at the Heart of ChatGPT and Bing, „Wired”, ISSN 1059-1028 [dostęp 2024-03-20] (ang.).
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worldcat.org

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