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This comprehensive review delves into the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs).
Language models are few-shot learners
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Mathqa: Towards interpretable math word problem solving with operation-based formalisms; 2019
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BLEU: a method for automatic evaluation of machine translation
Papineni K, Roukos S, Ward T, Zhu WJ · 2002
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Multi-step inference for reasoning over paragraphs; 2020
Liu J, Gardner M, Cohen SB, Lapata M · 2004
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ROUGE: a package for automatic evaluation of summaries
Chin-Yew L · 2004
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Mining and summarizing customer reviews
Hu M, Liu B · 2004
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Unsupervised commonsense question answering with self-talk; 2020
Shwartz V, West P, Bras RL, Bhagavatula C, Choi Y · 2004
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Reducing labeling effort for structured prediction tasks
Culotta A, McCallum A · 2005
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Evaluating evaluation methods for generation in the presence of variation
Stent A, Marge M, Singhai M · 2005
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METEOR: an automatic metric for MT evaluation with improved correlation with human judgments
Banerjee S, Lavie A · 2005
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Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales; 2005
Pang B, Lee L · 2005
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Re-evaluating the role of BLEU in machine translation research
Callison-Burch C, Osborne M, Koehn P · 2006
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Leveraging passage retrieval with generative models for open domain question answering; 2020
Izacard G, Grave E · 2007
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Some issues in automatic evaluation of English-Hindi MT: more blues for BLEU
Ananthakrishnan R, Bhattacharyya P, Sasikumar M, Shah RM · 2007
Earlier work this paper cites.
Leveraging passage retrieval with generative models for open domain question answering; 2020
Izacard G, Grave E · 2007
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University of Wisconsin-Madison Department of Computer Sciences
Settles B.: Active learning literature survey · 2009
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Measuring massive multitask language understanding; 2020
Hendrycks D, Burns C, Basart S, Zou A, Mazeika M, Song D, et al · 2009
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ImageNet: a large-scale hierarchical image database
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L · 2009
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Semantically-aligned universal tree-structured solver for math word problems; 2020
Qin J, Lin L, Liang X, Zhang R, Lin L · 2010
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Hogwild!: a lock-free approach to parallelizing stochastic gradient descent
Recht B, Re C, Wright S, Niu F · 2011
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng AY, et al · 2013
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Learning to Solve Arithmetic Word Problems with Verb Categorization
Hosseini MJ, Hajishirzi H, Etzioni O, Kushman N · 2014
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VQA: visual question answering
Antol S, Agrawal A, Lu J, Mitchell M, Batra D, Zitnick CL, et al · 2015
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Automatically solving number word problems by semantic parsing and reasoning
Shi S, Wang Y, Lin CY, Liu X, Rui Y · 2015
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Parsing algebraic word problems into equations
Koncel-Kedziorski R, Hajishirzi H, Sabharwal A, Etzioni O, Ang SD · 2015
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Character-level convolutional networks for text classification
Zhang X, Zhao J, LeCun Y · 2015
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Solving general arithmetic word problems
Roy S, Roth D · 2015
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Explaining and harnessing adversarial examples; 2015
Goodfellow IJ, Shlens J, Szegedy C · 2015
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MAWPS: a math word problem repository
Koncel-Kedziorski R, Roy S, Amini A, Kushman N, Hajishirzi H · 2016
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Solving general arithmetic word problems; 2016
Roy S, Roth D · 2016
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Modeling context in referring expressions
Yu L, Poirson P, Yang S, Berg AC, Berg TL · 2016
Earlier work this paper cites.
Generation and comprehension of unambiguous object descriptions
Mao J, Huang J, Toshev A, Camburu O, Yuille AL, Murphy K · 2016
Earlier work this paper cites.
Stealing machine learning models via prediction APIs
Tramèr F, Zhang F, Juels A, Reiter MK, Ristenpart T · 2016
Earlier work this paper cites.
Attention is all you need
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al · 2017
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Deep reinforcement learning from human preferences
Christiano PF, Leike J, Brown T, Martic M, Legg S, Amodei D · 2017
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Controlling linguistic style aspects in neural language generation
Ficler J, Goldberg Y · 2017
Earlier work this paper cites.
Visual question answering: a survey of methods and datasets
Wu Q, Teney D, Wang P, Shen C, Dick A, van den Hengel A · 2017
Earlier work this paper cites.
Visual question answering: datasets, algorithms, and future challenges
Kafle K, Kanan C · 2017
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Deep neural solver for math word problems
Wang Y, Liu X, Shi S · 2017
Earlier work this paper cites.
Unit dependency graph and its application to arithmetic word problem solving
Roy S, Roth D · 2017
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Conceptnet 5.5: An open multilingual graph of general knowledge
Speer R, Chin J, Havasi C · 2017
Earlier work this paper cites.
Certified defenses for data poisoning attacks; 2017
Steinhardt J, Koh PW, Liang P · 2017
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning; 2017
Chen X, Liu C, Li B, Lu K, Song D · 2017
Earlier work this paper cites.
Adversarial machine learning at scale; 2017
Kurakin A, Goodfellow I, Bengio S · 2017
Earlier work this paper cites.
Improving language understanding by generative pre-training; 2018
Radford A, Narasimhan K, Salimans T, Sutskever I, et al · 2018
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Joint learning embeddings for Chinese words and their components via ladder structured networks
YanSong S, JingLi Tencent A · 2018
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Hierarchical neural story generation
Fan A, Lewis M, Dauphin Y · 2018
Earlier work this paper cites.
Learning to write with cooperative discriminators
Holtzman A, Buys J, Forbes M, Bosselut A, Golub D, Choi Y · 2018
Earlier work this paper cites.
Hallucinations in neural machine translation; 2018
Lee K, Firat O, Agarwal A, Fannjiang C, Sussillo D · 2018
Earlier work this paper cites.
FVQA: fact-based visual question answering
Wang P, Wu Q, Shen C, Dick A, van den Hengel A · 2018
Earlier work this paper cites.
FEVER: a large-scale dataset for fact extraction and VERification; 2018
Thorne J, Vlachos A, Christodoulopoulos C, Mittal A · 2018
Earlier work this paper cites.
The narrativeqa reading comprehension challenge
Kočiskỳ T, Schwarz J, Blunsom P, Dyer C, Hermann KM, Melis G, et al · 2018
Earlier work this paper cites.
Commonsenseqa: a question answering challenge targeting commonsense knowledge; 2018
Talmor A, Herzig J, Lourie N, Berant J · 2018
Earlier work this paper cites.
HotpotQA: a dataset for diverse, explainable multi-hop question answering
Yang Z, Qi P, Zhang S, Bengio Y, Cohen WW, Salakhutdinov R, et al · 2018
Earlier work this paper cites.
Think you have solved question answering? try arc, the ai2 reasoning challenge; 2018
Clark P, Cowhey I, Etzioni O, Khot T, Sabharwal A, Schoenick C, et al · 2018
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SoK: security and privacy in machine learning
Papernot N, McDaniel P, Sinha A, Wellman MP · 2018
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Assessed: 2019-02-07
Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I, et al.: Language models are unsupervised multitask learners · 2019
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Unsupervised neural aspect extraction with sememes
Luo L, Ao X, Song Y, Li J, Yang X, He Q, et al · 2019
Earlier work this paper cites.
Exploring human-like reading strategy for abstractive text summarization
Yang M, Qu Q, Tu W, Shen Y, Zhao Z, Chen X · 2019
Earlier work this paper cites.
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
Talmor A, Herzig J, Lourie N, Berant J · 2019
Earlier work this paper cites.
BadNets: identifying vulnerabilities in the machine learning model supply chain; 2019
Gu T, Dolan-Gavitt B, Garg S · 2019
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Learning to summarize with human feedback
Stiennon N, Ouyang L, Wu J, Ziegler D, Lowe R, Voss C, et al · 2020
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The curious case of neural text degeneration
Holtzman A, Buys J, Du L, Forbes M, Choi Y · 2020
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Retrieval-augmented generation for knowledge-intensive NLP tasks
Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, et al · 2020
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BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, et al · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, et al · 2020
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Learning to summarize with human feedback
Stiennon N, Ouyang L, Wu J, Ziegler D, Lowe R, Voss C, et al · 2020
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BERTScore: evaluating text generation with BERT
Zhang T, Kishore V, Wu F, Weinberger KQ, Artzi Y · 2020
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Unsupervised data augmentation for consistency training
Xie Q, Dai Z, Hovy E, Luong MT, Le QV · 2020
Earlier work this paper cites.
Adversarial attacks and defenses in deep learning
Ren K, Zheng T, Qin Z, Liu X · 2020
Earlier work this paper cites.
How to backdoor federated learning
Bagdasaryan E, Veit A, Hua Y, Estrin D, Shmatikov V · 2020
Earlier work this paper cites.
Reflection backdoor: a natural backdoor attack on deep neural networks
Liu Y, Ma X, Bailey J, Lu F · 2020
Earlier work this paper cites.
Thieves on sesame street! Model extraction of BERT-based APIs
Krishna K, Tomar GS, Parikh AP, Papernot N, Iyyer M · 2020
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Explainable AI: a review of machine learning interpretability methods
Linardatos P, Papastefanopoulos V, Kotsiantis S · 2020
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Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
Amann J, Blasimme A, Vayena E, Frey D, Madai VI · 2020
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On the dangers of stochastic parrots: can language models be too big?
Bender EM, Gebru T, McMillan-Major A, Shmitchell S · 2021
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Zero-shot text-to-image generation; 2021
Ramesh A, Pavlov M, Goh G, Gray S, Voss C, Radford A, et al · 2021
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https://openai.com/index/dall-e/
OpenAI.: DALL·E: creating images from text · 2021
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Learning transferable visual models from natural language supervision
Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, et al · 2021
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Prompt programming for large language models: beyond the few-shot paradigm
Reynolds L, McDonell K · 2021
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Training verifiers to solve math word problems; 2021
Cobbe K, Kosaraju V, Bavarian M, Chen M, Jun H, Kaiser L, et al · 2021
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ProofWriter: generating implications, proofs, and abductive statements over natural language
Tafjord O, Dalvi B, Clark P · 2021
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Prefix-tuning: optimizing continuous prompts for generation
Li XL, Liang P · 2021
Earlier work this paper cites.
Recipes for building an open-domain chatbot
Roller S, Dinan E, Goyal N, Ju D, Williamson M, Liu Y, et al · 2021
Earlier work this paper cites.
Retrieval augmentation reduces hallucination in conversation; 2021
Shuster K, Poff S, Chen M, Kiela D, Weston J · 2021
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Recipes for building an open-domain chatbot
Roller S, Dinan E, Goyal N, Ju D, Williamson M, Liu Y, et al · 2021
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Scaling up visual and vision-language representation learning with noisy text supervision
Jia C, Yang Y, Xia Y, Chen YT, Parekh Z, Pham H, et al · 2021
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A diverse corpus for evaluating and developing English math word problem solvers; 2021
Miao SY, Liang CC, Su KY · 2021
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Measuring mathematical problem solving with the math dataset; 2021
Hendrycks D, Burns C, Kadavath S, Arora A, Basart S, Tang E, et al · 2021
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QuALITY: question answering with long input texts, yes!
Pang RY, Parrish A, Joshi N, Nangia N, Phang J, Chen A, et al · 2021
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Efficient attentions for long document summarization; 2021
Huang L, Cao S, Parulian N, Ji H, Wang L · 2021
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Summscreen: a dataset for abstractive screenplay summarization; 2021
Chen M, Chu Z, Wiseman S, Gimpel K · 2021
Earlier work this paper cites.
Dehghani M, Tay Y, Gritsenko AA, Zhao Z, Houlsby N, Diaz F, et al · 2021
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Dynabench: rethinking benchmarking in NLP; 2021
Kiela D, Bartolo M, Nie Y, Kaushik D, Geiger A, Wu Z, et al · 2021
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Cross-task generalization via natural language crowdsourcing instructions; 2021
Mishra S, Khashabi D, Baral C, Hajishirzi H · 2021
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Beyond goldfish memory: Long-term open-domain conversation; 2021
Xu J, Szlam A, Weston J · 2021
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FUDGE: controlled text generation with future discriminators
Yang K, Klein D · 2021
Earlier work this paper cites.
GPT3Mix: leveraging large-scale language models for text augmentation
Yoo KM, Park D, Kang J, Lee SW, Park W · 2021
Earlier work this paper cites.
Addressing adversarial machine learning attacks in smart healthcare perspectives; 2021
Selvakkumar A, Pal S, Jadidi Z · 2021
Cited alongside, same era.
Recent advances in adversarial training for adversarial robustness
Bai T, Luo J, Zhao J, Wen B, Wang Q · 2021
Cited alongside, same era.
What does artificial intelligence mean for organizations? A systematic review of organization studies research and a way forward
Öztürk D · 2021
Cited alongside, same era.
A very preliminary analysis of DALL-E 2; 2022
Marcus G, Davis E, Aaronson S · 2022
Cited alongside, same era.
Supervision exists everywhere: a data efficient contrastive language-image pre-training paradigm
Li Y, Liang F, Zhao L, Cui Y, Ouyang W, Shao J, et al · 2022
Cited alongside, same era.
APoLLo : unified adapter and prompt learning for vision language models
Chowdhury S, Nag S, Manocha D · 2023
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Self-regulating prompts: foundational model adaptation without forgetting; 2023
Khattak MU, Wasim ST, Naseer M, Khan S, Yang MH, Khan FS · 2023
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MaPLe: multi-modal prompt learning; 2023
Khattak MU, Rasheed H, Maaz M, Khan S, Khan FS · 2023
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A systematic survey of prompt engineering on vision-language foundation models; 2023
Gu J, Han Z, Chen S, Beirami A, He B, Zhang G, et al · 2023
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Flatness-aware prompt selection improves accuracy and sample efficiency; 2023
Shen L, Tan W, Zheng B, Khashabi D · 2023
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Fantastically ordered prompts and where to find them: overcoming few-shot prompt order sensitivity
Lu Y, Bartolo M, Moore A, Riedel S, Stenetorp P · 2022
Cited alongside, same era.
Do prompt-based models really understand the meaning of their prompts?
Webson A, Pavlick E · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Wei J, Wang X, Schuurmans D, Bosma M, Ichter B, Xia F, et al · 2022
Cited alongside, same era.
Prompt engineering for zero‐shot and few‐shot defect detection and classification using a visual‐language pretrained model
Yong G, Jeon K, Gil D, Lee G · 2022
Cited alongside, same era.
Cutting down on prompts and parameters: simple few-shot learning with language models
Logan IV R, Balažević I, Wallace E, Petroni F, Singh S, Riedel S · 2022
Cited alongside, same era.
Large language models are zero-shot reasoners
Kojima T, Gu SS, Reid M, Matsuo Y, Iwasawa Y · 2022
Cited alongside, same era.
Solving quantitative reasoning problems with language models
Lewkowycz A, Andreassen A, Dohan D, Dyer E, Michalewski H, Ramasesh V, et al · 2022
Cited alongside, same era.
Closest in time.
Refiner: reasoning feedback on intermediate representations; 2023
Paul D, Ismayilzada M, Peyrard M, Borges B, Bosselut A, West R, et al · 2023
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From sparse to dense: GPT-4 summarization with chain of density prompting; 2023
Adams G, Fabbri A, Ladhak F, Lehman E, Elhadad N · 2023
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Enhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting; 2023
Wang R, Wang H, Mi F, Chen Y, Xue B, Wong KF, et al · 2023
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Beyond the imitation game: quantifying and extrapolating the capabilities of language models; 2023
Srivastava A, Rastogi A, Rao A, Shoeb AAM, Abid A, Fisch A, et al · 2023
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Let’s verify step by step; 2023
Lightman H, Kosaraju V, Burda Y, Edwards H, Baker B, Lee T, et al · 2023
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Knowledge card: filling LLMs’ knowledge gaps with plug-in specialized Language Models; 2023
Feng S, Shi W, Bai Y, Balachandran V, He T, Tsvetkov Y · 2023
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Sentiment analysis in the era of large language models: a reality check; 2023
Zhang W, Deng Y, Liu B, Pan SJ, Bing L · 2023
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Less likely brainstorming: Using language models to generate alternative hypotheses
Tang L, Peng Y, Wang Y, Ding Y, Durrett G, Rousseau JF · 2023
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Bring your own data! Self-supervised evaluation for large language models; 2023
Jain N, Saifullah K, Wen Y, Kirchenbauer J, Shu M, Saha A, et al · 2023
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PandaLM: an automatic evaluation benchmark for LLM instruction tuning optimization; 2023
Wang Y, Yu Z, Zeng Z, Yang L, Wang C, Chen H, et al · 2023
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Lin YT, Chen YN · 2023
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Reprompting: automated chain-of-thought prompt inference through gibbs sampling; 2023
Xu W, Banburski-Fahey A, Jojic N · 2023
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Wang L, Xu W, Lan Y, Hu Z, Lan Y, Lee RKW, et al · 2023
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Automatic prompt optimization with ”gradient descent” and beam search; 2023
Pryzant R, Iter D, Li J, Lee YT, Zhu C, Zeng M · 2023
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Walking down the memory maze: beyond context limit through interactive reading; 2023
Chen H, Pasunuru R, Weston J, Celikyilmaz A · 2023
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Adapting language models to compress contexts; 2023
Chevalier A, Wettig A, Ajith A, Chen D · 2023
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Scaling transformer to 1m tokens and beyond with rmt; 2023
Bulatov A, Kuratov Y, Kapushev Y, Burtsev MS · 2023
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Guo Q, Wang R, Guo J, Li B, Song K, Tan X, et al · 2023
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Hierarchical prompting assists large language model on web navigation; 2023
Sridhar A, Lo R, Xu FF, Zhu H, Zhou S · 2023
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TreePrompt: Learning to Compose Tree Prompts for Explainable Visual Grounding; 2023
Zhang C, Xiao J, Chen L, Shao J, Chen L · 2023
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Finite reservoirs and irreversibility corrections to Hamiltonian systems statistics; 2023
Colangeli M, Di Francesco A, Rondoni L · 2023
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Llmlingua: Compressing prompts for accelerated inference of large language models; 2023
Jiang H, Wu Q, Lin CY, Yang Y, Qiu L · 2023
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Skeleton-of-thought: Large language models can do parallel decoding; 2023
Ning X, Lin Z, Zhou Z, Yang H, Wang Y · 2023
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Chain-of-symbol prompting elicits planning in Large Langauge Models; 2023
Hu H, Lu H, Zhang H, Song YZ, Lam W, Zhang Y · 2023
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Knowledge Card: Filling LLMs’ Knowledge Gaps with Plug-in Specialized Language Models; 2023
Feng S, Shi W, Bai Y, Balachandran V, He T, Tsvetkov Y · 2023
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Pearl: Prompting large language models to plan and execute actions over long documents; 2023
Sun S, Liu Y, Wang S, Zhu C, Iyyer M · 2023
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Large Language Models Are Human-Level Prompt Engineers; 2023
Zhou Y, Muresanu AI, Han Z, Paster K, Pitis S, Chan H, et al · 2023
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InstructEval: systematic evaluation of instruction selection methods; 2023
Ajith A, Pan C, Xia M, Deshpande A, Narasimhan K · 2023
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ML4STEM professional development program: enriching K-12 STEM teaching with machine learning
Tang J, Zhou X, Wan X, Daley M, Bai Z · 2023
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Machine learning and Hebrew NLP for automated assessment of open-ended questions in biology
Ariely M, Nazaretsky T, Alexandron G · 2023
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GPT-4 as an automatic grader: the accuracy of grades set by GPT-4 on introductory programming assignments [Bachelor Thesis]
Nilsson F, Tuvstedt J · 2023
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Towards trustworthy autograding of short, multi-lingual, multi-type answers
Schneider J, Richner R, Riser M · 2023
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DOC: improving long story coherence with detailed outline control
Yang K, Klein D, Peng N, Tian Y · 2023
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Repository-level prompt generation for large language models of code
Shrivastava D, Larochelle H, Tarlow D · 2023
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Making language models better reasoners with step-aware verifier
Li Y, Lin Z, Zhang S, Fu Q, Chen B, Lou JG, et al · 2023
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Available from: https://www.infosectrain.com/blog/ai-at-risk-owasp-top-10-critical-vulnerabilities-for-large-language-models-llms/
Rawat P.: AI at risk: OWASP top 10 critical vulnerabilities for large language models (LLMs) · 2023
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Adversarial demonstration attacks on large language models; 2023
Wang J, Liu Z, Park KH, Jiang Z, Zheng Z, Wu Z, et al · 2023
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Available from: https://orc.mit.edu/events/discrete-optimization-adversarial-attacks-large-language-models
Kolter Z.: Discrete optimization for adversarial attacks on large language models · 2023
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Survey of vulnerabilities in large language models revealed by adversarial attacks; 2023
Shayegani E, Mamun MAA, Fu Y, Zaree P, Dong Y, Abu-Ghazaleh N · 2023
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Prompt as triggers for backdoor attack: examining the vulnerability in language models
Zhao S, Wen J, Luu A, Zhao J, Fu J · 2023
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Prompt engineering for large language models; 2023
Gao A · 2023
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Ignore this title and HackAPrompt: exposing systemic vulnerabilities of LLMs through a global prompt hacking competition
Schulhoff S, Pinto J, Khan A, Bouchard LF, Si C, Anati S, et al · 2023
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Available from: https://medium.com/@alexandre.allouin/understanding-llm-prompt-hacking-and-attacks-8781c313a25b
Allouin A.: Understanding LLM prompt hacking and attacks · 2023
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Prompt stealing attacks against text-to-image generation models; 2023
Shen X, Qu Y, Backes M, Zhang Y · 2023
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Stealing the decoding algorithms of language models
Naseh A, Krishna K, Iyyer M, Houmansadr A · 2023
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Obtaining genetics insights from deep learning via explainable artificial intelligence
Novakovsky G, Dexter N, Libbrecht MW, Wasserman WW, Mostafavi S · 2023
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The AI revolution: opportunities and challenges for the finance sector; 2023
Maple C, Szpruch L, Epiphaniou G, Staykova K, Singh S, Penwarden W, et al · 2023
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If our aim is to build morality into an artificial agent, how might we begin to go about doing so?
Seeamber R, Badea C · 2023
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OpenAI, Achiam J, Adler S, Agarwal S, Ahmad L, Akkaya I, et al · 2024
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Gemini: a family of highly capable multimodal models; 2024
Team G, Anil R, Borgeaud S, Alayrac JB, Yu J, Soricut R, et al · 2024
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Available from: https://blog.google/technology/ai/google-gemini-next-generation-model-february-2024/#sundar-note
Google.: Google Gemini: next-generation model · 2024
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The Claude 3 model family: Opus, Sonnet, Haiku
Anthropic · 2024
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Available from: https://www.anthropic.com/news/claude-3-family
Anthropic.: Claude 3 model · 2024
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The Llama 3 herd of models; 2024
Dubey A, Jauhri A, Pandey A, Kadian A, Al-Dahle A, Letman A, et al · 2024
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Accessed: 2024-08-04
OpenAI.: Hello GPT-4o · 2024
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Accessed: 2024-05-22
Moore O.: Announcing GPT-4o in the API! · 2024
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Rethinking open source generative AI: open washing and the EU AI Act
Liesenfeld A, Dingemanse M · 2024
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Active prompting with chain-of-thought for large language models; 2024
Diao S, Wang P, Lin Y, Zhang T · 2024
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Sahoo P, Singh AK, Saha S, Jain V, Mondal S, Chadha A · 2024
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Exploring prompt engineering practices in the enterprise; 2024
Desmond M, Brachman M · 2024
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