- See Also
-
Links
- “Solving Challenging Math Word Problems Using GPT-4 Code Interpreter With Code-based Self-Verification”, Zhou et al 2023
- “Testing GPT-4 With Wolfram Alpha and Code Interpreter Plug-ins on Math and Science Problems”, Davis & Aaronson 2023
- “Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow”, Rio-Chanona et al 2023
- “Explaining Competitive-Level Programming Solutions Using LLMs”, Li et al 2023
- “AI Is a Lot of Work: As the Technology Becomes Ubiquitous, a Vast Tasker Underclass Is Emerging—and Not Going Anywhere”, Dzieza 2023
- “CodeCompose: A Large-Scale Industrial Deployment of AI-assisted Code Authoring”, Murali et al 2023
- “Chatting With GPT-3 for Zero-Shot Human-Like Mobile Automated GUI Testing”, Liu et al 2023
- “Large Language Model Programs”, Schlag et al 2023
- “Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding”, Xie et al 2023
- “LLM+P: Empowering Large Language Models With Optimal Planning Proficiency”, Liu et al 2023
- “How Secure Is Code Generated by ChatGPT?”, Khoury et al 2023
- “Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes”, Arora et al 2023
- “Today Was the First Day That I Could Definitively Say That GPT-4 Has Saved Me a Substantial Amount of Tedious Work”, Tao 2023
- “Introducing Microsoft 365 Copilot—your Copilot for Work”, Spataro 2023
- “Large Language Models and Simple, Stupid Bugs”, Jesse et al 2023
- “ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”, Azerbayev et al 2023
- “CodeBERTScore: Evaluating Code Generation With Pretrained Models of Code”, Zhou et al 2023
- “Google Is Asking Employees to Test Potential ChatGPT Competitors, including a Chatbot Called 'Apprentice Bard'”, Elias 2023
- “Large Language Models Are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning”, Ye et al 2023
- “Faithful Chain-of-Thought Reasoning”, Lyu et al 2023
- “An Analysis of the Automatic Bug Fixing Performance of ChatGPT”, Sobania et al 2023
- “Connor Leahy on Aliens, Ethics, Economics, Memetics, and Education § GPT-4”, Leahy 2023
- “General Availability of Azure OpenAI Service Expands Access to Large, Advanced AI Models With Added Enterprise Benefits”, Boyd 2023
- “SantaCoder: Don’t Reach for the Stars!”, Allal et al 2023
- “TrojanPuzzle: Covertly Poisoning Code-Suggestion Models”, Aghakhani et al 2023
- “ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages”, Chai et al 2022
- “The Stack: 3 TB of Permissively Licensed Source Code”, Kocetkov et al 2022
- “PAL: Program-aided Language Models”, Gao et al 2022
- “Programming Possibility: Kevin Scott on AI’s Impact on Cognitive Work”, Hoffman & Scott 2022
- “Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them”, Suzgun et al 2022
- “Vote-K: Selective Annotation Makes Language Models Better Few-Shot Learners”, Su et al 2022
- “Repair Is Nearly Generation: Multilingual Program Repair With LLMs”, Joshi et al 2022
- “Language Models Can Teach Themselves to Program Better”, Haluptzok et al 2022
- “Efficient Training of Language Models to Fill in the Middle”, Bavarian et al 2022
- “PanGu-Coder: Program Synthesis With Function-Level Language Modeling”, Christopoulou et al 2022
- “CodeT: Code Generation With Generated Tests”, Chen et al 2022
- “Can Large Language Models Reason about Medical Questions?”, Liévin et al 2022
- “Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code”, Volum et al 2022
- “Code Translation With Compiler Representations”, Szafraniec et al 2022
- “Repository-Level Prompt Generation for Large Language Models of Code”, Shrivastava et al 2022
- “Learning to Model Editing Processes”, Reid & Neubig 2022
- “Productivity Assessment of Neural Code Completion”, Ziegler et al 2022
- “End-to-end Symbolic Regression With Transformers”, Kamienny et al 2022
- “InCoder: A Generative Model for Code Infilling and Synthesis”, Fried et al 2022
- “PaLM: Scaling Language Modeling With Pathways”, Chowdhery et al 2022
- “A Conversational Paradigm for Program Synthesis”, Nijkamp et al 2022
- “Evaluating the Text-to-SQL Capabilities of Large Language Models”, Rajkumar et al 2022
- “Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models”, Vaithilingam et al 2022
- “PolyCoder: A Systematic Evaluation of Large Language Models of Code”, Xu et al 2022
- “Pop Quiz! Can a Large Language Model Help With Reverse Engineering?”, Pearce et al 2022
- “Text and Code Embeddings by Contrastive Pre-Training”, Neelakantan et al 2022
- “Neural Language Models Are Effective Plagiarists”, Biderman & Raff 2022
- “Deep Symbolic Regression for Recurrent Sequences”, d’Ascoli et al 2022
- “Discovering the Syntax and Strategies of Natural Language Programming With Generative Language Models”, Jiang et al 2022
- “A Neural Network Solves and Generates Mathematics Problems by Program Synthesis: Calculus, Differential Equations, Linear Algebra, and More”, Drori et al 2021
- “WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing”, Hilton et al 2021
- “WebGPT: Browser-assisted Question-answering With Human Feedback”, Nakano et al 2021
- “Few-Shot Semantic Parsing With Language Models Trained On Code”, Shin & Durme 2021
- “Scaling Language Models: Methods, Analysis & Insights from Training Gopher”, Rae et al 2021
- “Jigsaw: Large Language Models Meet Program Synthesis”, Jain et al 2021
- “Can Pre-trained Language Models Be Used to Resolve Textual and Semantic Merge Conflicts?”, Zhang et al 2021
- “Solving Probability and Statistics Problems by Program Synthesis”, Tang et al 2021
- “Solving Linear Algebra by Program Synthesis”, Drori & Verma 2021
- “Automatic Program Repair With OpenAI’s Codex: Evaluating QuixBugs”, Prenner & Robbes 2021
- “GenLine and GenForm: Two Tools for Interacting With Generative Language Models in a Code Editor”, Jiang et al 2021b
- “An Empirical Cybersecurity Evaluation of GitHub Copilot’s Code Contributions”, Pearce et al 2021
- “Learning C to X86 Translation: An Experiment in Neural Compilation”, Armengol-Estapé & O’Boyle 2021
- “Program Synthesis With Large Language Models”, Austin et al 2021
- “TAPEX: Table Pre-training via Learning a Neural SQL Executor”, Liu et al 2021
- “Evaluating Large Language Models Trained on Code”, Chen et al 2021
- “Research Recitation: A First Look at Rote Learning in GitHub Copilot Suggestions”, Ziegler 2021
- “Microsoft and OpenAI Have a New A.I. Tool That Will Give Coding Suggestions to Software Developers”, Novet 2021
- “SymbolicGPT: A Generative Transformer Model for Symbolic Regression”, Valipour et al 2021
- “Measuring Coding Challenge Competence With APPS”, Hendrycks et al 2021
- “Improving Code Autocompletion With Transfer Learning”, Zhou et al 2021
- “LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning”, Wu et al 2021
- “Learning Autocompletion from Real-World Datasets”, Aye et al 2020
- “GraphCodeBERT: Pre-training Code Representations With Data Flow”, Guo et al 2020
- “CoCoNuT: Combining Context-Aware Neural Translation Models Using Ensemble for Program Repair”, Lutellier et al 2020
- “TransCoder: Unsupervised Translation of Programming Languages”, Lachaux et al 2020
- “GPT-3 Random Sample Dump: JavaScript Tutorial”, GPT-3 2020
- “IntelliCode Compose: Code Generation Using Transformer”, Svyatkovskiy et al 2020
- “Deep Learning for Symbolic Mathematics”, Lample & Charton 2019
- “CodeSearchNet Challenge: Evaluating the State of Semantic Code Search”, Husain et al 2019
- “BERTScore: Evaluating Text Generation With BERT”, Zhang et al 2019
- “Seq2SQL: Generating Structured Queries from Natural Language Using Reinforcement Learning”, Zhong et al 2017
- “Learning to Superoptimize Programs”, Bunel et al 2017
- “DeepCoder: Learning to Write Programs”, Balog et al 2016
- “Neural Programmer-Interpreters”, Reed & Freitas 2015
- “OpenAI API Alchemy: Smart Formatting and Code Creation”
- “Transformer-VAE for Program Synthesis”
- Sort By Magic
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“Solving Challenging Math Word Problems Using GPT-4 Code Interpreter With Code-based Self-Verification”, Zhou et al 2023
“Testing GPT-4 With Wolfram Alpha and Code Interpreter Plug-ins on Math and Science Problems”, Davis & Aaronson 2023
“Testing GPT-4 with Wolfram Alpha and Code Interpreter plug-ins on math and science problems”
“Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow”, Rio-Chanona et al 2023
“Explaining Competitive-Level Programming Solutions Using LLMs”, Li et al 2023
“Explaining Competitive-Level Programming Solutions using LLMs”
“AI Is a Lot of Work: As the Technology Becomes Ubiquitous, a Vast Tasker Underclass Is Emerging—and Not Going Anywhere”, Dzieza 2023
“CodeCompose: A Large-Scale Industrial Deployment of AI-assisted Code Authoring”, Murali et al 2023
“CodeCompose: A Large-Scale Industrial Deployment of AI-assisted Code Authoring”
“Chatting With GPT-3 for Zero-Shot Human-Like Mobile Automated GUI Testing”, Liu et al 2023
“Chatting with GPT-3 for Zero-Shot Human-Like Mobile Automated GUI Testing”
“Large Language Model Programs”, Schlag et al 2023
“Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding”, Xie et al 2023
“Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding”
“LLM+P: Empowering Large Language Models With Optimal Planning Proficiency”, Liu et al 2023
“LLM+P: Empowering Large Language Models with Optimal Planning Proficiency”
“How Secure Is Code Generated by ChatGPT?”, Khoury et al 2023
“Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes”, Arora et al 2023
“Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes”
“Today Was the First Day That I Could Definitively Say That GPT-4 Has Saved Me a Substantial Amount of Tedious Work”, Tao 2023
“Introducing Microsoft 365 Copilot—your Copilot for Work”, Spataro 2023
“Large Language Models and Simple, Stupid Bugs”, Jesse et al 2023
“ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”, Azerbayev et al 2023
“ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”
“CodeBERTScore: Evaluating Code Generation With Pretrained Models of Code”, Zhou et al 2023
“CodeBERTScore: Evaluating Code Generation with Pretrained Models of Code”
“Google Is Asking Employees to Test Potential ChatGPT Competitors, including a Chatbot Called 'Apprentice Bard'”, Elias 2023
“Large Language Models Are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning”, Ye et al 2023
“Faithful Chain-of-Thought Reasoning”, Lyu et al 2023
“An Analysis of the Automatic Bug Fixing Performance of ChatGPT”, Sobania et al 2023
“An Analysis of the Automatic Bug Fixing Performance of ChatGPT”
“Connor Leahy on Aliens, Ethics, Economics, Memetics, and Education § GPT-4”, Leahy 2023
“Connor Leahy on Aliens, Ethics, Economics, Memetics, and Education § GPT-4”
“General Availability of Azure OpenAI Service Expands Access to Large, Advanced AI Models With Added Enterprise Benefits”, Boyd 2023
“SantaCoder: Don’t Reach for the Stars!”, Allal et al 2023
“TrojanPuzzle: Covertly Poisoning Code-Suggestion Models”, Aghakhani et al 2023
“ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages”, Chai et al 2022
“ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages”
“The Stack: 3 TB of Permissively Licensed Source Code”, Kocetkov et al 2022
“PAL: Program-aided Language Models”, Gao et al 2022
“Programming Possibility: Kevin Scott on AI’s Impact on Cognitive Work”, Hoffman & Scott 2022
“Programming Possibility: Kevin Scott on AI’s Impact on Cognitive Work”
“Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them”, Suzgun et al 2022
“Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them”
“Vote-K: Selective Annotation Makes Language Models Better Few-Shot Learners”, Su et al 2022
“Vote-K: Selective Annotation Makes Language Models Better Few-Shot Learners”
“Repair Is Nearly Generation: Multilingual Program Repair With LLMs”, Joshi et al 2022
“Repair Is Nearly Generation: Multilingual Program Repair with LLMs”
“Language Models Can Teach Themselves to Program Better”, Haluptzok et al 2022
“Efficient Training of Language Models to Fill in the Middle”, Bavarian et al 2022
“Efficient Training of Language Models to Fill in the Middle”
“PanGu-Coder: Program Synthesis With Function-Level Language Modeling”, Christopoulou et al 2022
“PanGu-Coder: Program Synthesis with Function-Level Language Modeling”
“CodeT: Code Generation With Generated Tests”, Chen et al 2022
“Can Large Language Models Reason about Medical Questions?”, Liévin et al 2022
“Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code”, Volum et al 2022
“Code Translation With Compiler Representations”, Szafraniec et al 2022
“Repository-Level Prompt Generation for Large Language Models of Code”, Shrivastava et al 2022
“Repository-Level Prompt Generation for Large Language Models of Code”
“Learning to Model Editing Processes”, Reid & Neubig 2022
“Productivity Assessment of Neural Code Completion”, Ziegler et al 2022
“End-to-end Symbolic Regression With Transformers”, Kamienny et al 2022
“InCoder: A Generative Model for Code Infilling and Synthesis”, Fried et al 2022
“InCoder: A Generative Model for Code Infilling and Synthesis”
“PaLM: Scaling Language Modeling With Pathways”, Chowdhery et al 2022
“A Conversational Paradigm for Program Synthesis”, Nijkamp et al 2022
“Evaluating the Text-to-SQL Capabilities of Large Language Models”, Rajkumar et al 2022
“Evaluating the Text-to-SQL Capabilities of Large Language Models”
“Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models”, Vaithilingam et al 2022
“PolyCoder: A Systematic Evaluation of Large Language Models of Code”, Xu et al 2022
“PolyCoder: A Systematic Evaluation of Large Language Models of Code”
“Pop Quiz! Can a Large Language Model Help With Reverse Engineering?”, Pearce et al 2022
“Pop Quiz! Can a Large Language Model Help With Reverse Engineering?”
“Text and Code Embeddings by Contrastive Pre-Training”, Neelakantan et al 2022
“Neural Language Models Are Effective Plagiarists”, Biderman & Raff 2022
“Deep Symbolic Regression for Recurrent Sequences”, d’Ascoli et al 2022
“Discovering the Syntax and Strategies of Natural Language Programming With Generative Language Models”, Jiang et al 2022
“A Neural Network Solves and Generates Mathematics Problems by Program Synthesis: Calculus, Differential Equations, Linear Algebra, and More”, Drori et al 2021
“WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing”, Hilton et al 2021
“WebGPT: Improving the factual accuracy of language models through web browsing”
“WebGPT: Browser-assisted Question-answering With Human Feedback”, Nakano et al 2021
“WebGPT: Browser-assisted question-answering with human feedback”
“Few-Shot Semantic Parsing With Language Models Trained On Code”, Shin & Durme 2021
“Few-Shot Semantic Parsing with Language Models Trained On Code”
“Scaling Language Models: Methods, Analysis & Insights from Training Gopher”, Rae et al 2021
“Scaling Language Models: Methods, Analysis & Insights from Training Gopher”
“Jigsaw: Large Language Models Meet Program Synthesis”, Jain et al 2021
“Can Pre-trained Language Models Be Used to Resolve Textual and Semantic Merge Conflicts?”, Zhang et al 2021
“Can Pre-trained Language Models be Used to Resolve Textual and Semantic Merge Conflicts?”
“Solving Probability and Statistics Problems by Program Synthesis”, Tang et al 2021
“Solving Probability and Statistics Problems by Program Synthesis”
“Solving Linear Algebra by Program Synthesis”, Drori & Verma 2021
“Automatic Program Repair With OpenAI’s Codex: Evaluating QuixBugs”, Prenner & Robbes 2021
“Automatic Program Repair with OpenAI’s Codex: Evaluating QuixBugs”
“GenLine and GenForm: Two Tools for Interacting With Generative Language Models in a Code Editor”, Jiang et al 2021b
“GenLine and GenForm: Two Tools for Interacting with Generative Language Models in a Code Editor”
“An Empirical Cybersecurity Evaluation of GitHub Copilot’s Code Contributions”, Pearce et al 2021
“An Empirical Cybersecurity Evaluation of GitHub Copilot’s Code Contributions”
“Learning C to X86 Translation: An Experiment in Neural Compilation”, Armengol-Estapé & O’Boyle 2021
“Learning C to x86 Translation: An Experiment in Neural Compilation”
“Program Synthesis With Large Language Models”, Austin et al 2021
“TAPEX: Table Pre-training via Learning a Neural SQL Executor”, Liu et al 2021
“TAPEX: Table Pre-training via Learning a Neural SQL Executor”
“Evaluating Large Language Models Trained on Code”, Chen et al 2021
“Research Recitation: A First Look at Rote Learning in GitHub Copilot Suggestions”, Ziegler 2021
“Research recitation: A first look at rote learning in GitHub Copilot suggestions”
“Microsoft and OpenAI Have a New A.I. Tool That Will Give Coding Suggestions to Software Developers”, Novet 2021
“Microsoft and OpenAI have a new A.I. tool that will give coding suggestions to software developers”
“SymbolicGPT: A Generative Transformer Model for Symbolic Regression”, Valipour et al 2021
“SymbolicGPT: A Generative Transformer Model for Symbolic Regression”
“Measuring Coding Challenge Competence With APPS”, Hendrycks et al 2021
“Improving Code Autocompletion With Transfer Learning”, Zhou et al 2021
“LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning”, Wu et al 2021
“LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning”
“Learning Autocompletion from Real-World Datasets”, Aye et al 2020
“GraphCodeBERT: Pre-training Code Representations With Data Flow”, Guo et al 2020
“GraphCodeBERT: Pre-training Code Representations with Data Flow”
“CoCoNuT: Combining Context-Aware Neural Translation Models Using Ensemble for Program Repair”, Lutellier et al 2020
“CoCoNuT: Combining Context-Aware Neural Translation Models using Ensemble for Program Repair”
“TransCoder: Unsupervised Translation of Programming Languages”, Lachaux et al 2020
“TransCoder: Unsupervised Translation of Programming Languages”
“GPT-3 Random Sample Dump: JavaScript Tutorial”, GPT-3 2020
“IntelliCode Compose: Code Generation Using Transformer”, Svyatkovskiy et al 2020
“Deep Learning for Symbolic Mathematics”, Lample & Charton 2019
“CodeSearchNet Challenge: Evaluating the State of Semantic Code Search”, Husain et al 2019
“CodeSearchNet Challenge: Evaluating the State of Semantic Code Search”
“BERTScore: Evaluating Text Generation With BERT”, Zhang et al 2019
“Seq2SQL: Generating Structured Queries from Natural Language Using Reinforcement Learning”, Zhong et al 2017
“Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning”
“Learning to Superoptimize Programs”, Bunel et al 2017
“DeepCoder: Learning to Write Programs”, Balog et al 2016
“Neural Programmer-Interpreters”, Reed & Freitas 2015
“OpenAI API Alchemy: Smart Formatting and Code Creation”
“Transformer-VAE for Program Synthesis”
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
programming-language-models
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Wikipedia
Miscellaneous
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/doc/ai/nn/transformer/gpt/codex/2023-01-16-microsoft-timelineofairesearchandproducts.png -
/doc/ai/nn/transformer/gpt/codex/2021-nakano-figure7-bestfnscalingbyflopsandanswerssampled.png -
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https://andrewmayne.com/2023/03/23/chatgpt-code-interpreter-magic/ -
https://borretti.me/article/astronomical-calculations-for-hard-sf-common-lisp -
https://builtin.com/job/customer-success/expert-ai-teacher-contract/1267315 -
https://gist.github.com/harryaskham/68a611bef777525991790bca2f2d324d -
https://github.com/E-xyza/Exonerate/blob/master/bench/reports/gpt-bench.md -
https://lemire.me/blog/2023/03/22/can-gpt-pass-my-programming-courses/ -
https://medium.com/tenable-techblog/g-3po-a-protocol-droid-for-ghidra-4b46fa72f1ff -
https://micahflee.com/2023/04/capturing-the-flag-with-gpt-4/ -
https://openai.com/blog/function-calling-and-other-api-updates#function-calling -
https://openai.com/blog/introducing-text-and-code-embeddings/ -
https://platform.openai.com/docs/guides/embeddings/code-search-using-embeddings -
https://platform.openai.com/docs/guides/embeddings/use-cases -
https://research.checkpoint.com/2023/opwnai-cybercriminals-starting-to-use-chatgpt/ -
https://stability.ai/blog/stablecode-llm-generative-ai-coding -
https://statmodeling.stat.columbia.edu/2023/04/18/chatgpt4-writes-stan-code-so-i-dont-have-to/ -
https://statmodeling.stat.columbia.edu/2023/08/20/bob-carpenter-thinks-gpt-4-is-awesome/ -
https://tagide.com/education/writing-a-tokenizer-with-chatgpt/ -
https://towardsdatascience.com/can-chatgpt-write-better-sql-than-a-data-analyst-f079518efab2 -
https://towardsdatascience.com/codex-by-openai-in-action-83529c0076cc -
https://twitter.com/AlexKontorovich/status/1678772963183820801 -
https://twitter.com/AlexKontorovich/status/1678772964836397056 -
https://twitter.com/DaveMonlander/status/1612802240582135809 -
https://twitter.com/GabriellaG439/status/1561007332267421696 -
https://twitter.com/PerksPlus0001/status/1631372820709253120 -
https://twitter.com/andrewwhite01/status/1616933106786738176 -
https://twitter.com/atlantis__labs/status/1677782219937525760 -
https://twitter.com/fabianstelzer/status/1572571003804614657 -
https://twitter.com/francoisfleuret/status/1699117856779075949 -
https://twitter.com/jamesbrandecon/status/1639709460762624001 -
https://twitter.com/jeremyphoward/status/1688793283034779648 -
https://twitter.com/kenshinsamurai9/status/1662510532585291779 -
https://twitter.com/moreisdifferent/status/1612489352105365511 -
https://twitter.com/patrickmineault/status/1591874392279351297 -
https://twitter.com/scottleibrand/status/1430753899460194310 -
https://twitter.com/sergeykarayev/status/1569377881440276481 -
https://twitter.com/sergeykarayev/status/1569571367833714688 -
https://twitter.com/sharifshameem/status/1672852345259180037 -
https://twitter.com/yoheinakajima/status/1670557048743010305 -
https://www.honeycomb.io/blog/hard-stuff-nobody-talks-about-llm -
https://www.lesswrong.com/posts/KSroBnxCHodGmPPJ8/jailbreaking-gpt-4-s-code-interpreter -
https://www.lesswrong.com/posts/ib9bfyJiz4FLuHDQs/openai-codex-first-impressions -
https://www.lesswrong.com/posts/ux93sLHcqmBfsRTvg/gpt-can-write-quines-now-gpt-4 -
https://www.nytimes.com/2021/09/09/technology/codex-artificial-intelligence-coding.html -
https://www.oneusefulthing.org/p/it-is-starting-to-get-strange -
https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/ -
https://www.reddit.com/r/ChatGPT/comments/12a0ajb/i_gave_gpt4_persistent_memory_and_the_ability_to/ -
https://www.reddit.com/r/GPT3/comments/106t5gv/compressing_prompt_text_with_lossless_compression/ -
https://www.samdickie.me/writing/experiment-1-creating-a-landing-page-using-ai-tools-no-code -
https://www.shawnmatthewcrawford.com/balloons-the-balloon-clicker-game.html -
https://www.theverge.com/2021/8/10/22618128/openai-codex-natural-language-into-code-api-beta-access -
https://xenaproject.wordpress.com/2022/09/12/beyond-the-liquid-tensor-experiment/
Link Bibliography
-
https://arxiv.org/abs/2308.07921: “Solving Challenging Math Word Problems Using GPT-4 Code Interpreter With Code-based Self-Verification”, -
https://www.theverge.com/features/23764584/ai-artificial-intelligence-data-notation-labor-scale-surge-remotasks-openai-chatbots: “AI Is a Lot of Work: As the Technology Becomes Ubiquitous, a Vast Tasker Underclass Is Emerging—and Not Going Anywhere”, Josh Dzieza -
https://blogs.microsoft.com/blog/2023/03/16/introducing-microsoft-365-copilot-your-copilot-for-work/: “Introducing Microsoft 365 Copilot—your Copilot for Work”, Jared Spataro -
https://arxiv.org/abs/2303.11455: “Large Language Models and Simple, Stupid Bugs”, Kevin Jesse, Toufique Ahmed, Premkumar T. Devanbu, Emily Morgan -
https://arxiv.org/abs/2302.12433: “ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics”, Zhangir Azerbayev, Bartosz Piotrowski, Hailey Schoelkopf, Edward W. Ayers, Dragomir Radev, Jeremy Avigad -
https://www.cnbc.com/2023/01/31/google-testing-chatgpt-like-chatbot-apprentice-bard-with-employees.html: “Google Is Asking Employees to Test Potential ChatGPT Competitors, including a Chatbot Called 'Apprentice Bard'”, Jennifer Elias -
https://arxiv.org/abs/2301.08653: “An Analysis of the Automatic Bug Fixing Performance of ChatGPT”, Dominik Sobania, Martin Briesch, Carol Hanna, Justyna Petke -
https://azure.microsoft.com/en-us/blog/general-availability-of-azure-openai-service-expands-access-to-large-advanced-ai-models-with-added-enterprise-benefits/: “General Availability of Azure OpenAI Service Expands Access to Large, Advanced AI Models With Added Enterprise Benefits”, Eric Boyd -
https://arxiv.org/abs/2211.15533: “The Stack: 3 TB of Permissively Licensed Source Code”, -
https://greylock.com/greymatter/kevin-scott-ai-programming-possibility/: “Programming Possibility: Kevin Scott on AI’s Impact on Cognitive Work”, Reid Hoffman, Kevin Scott -
https://arxiv.org/abs/2210.09261#google: “Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them”, -
https://arxiv.org/abs/2209.01975: “Vote-<em>K</em>: Selective Annotation Makes Language Models Better Few-Shot Learners”, -
https://arxiv.org/abs/2207.08143: “Can Large Language Models Reason about Medical Questions?”, Valentin Liévin, Christoffer Egeberg Hother, Ole Winther -
https://arxiv.org/abs/2205.06537#github: “Productivity Assessment of Neural Code Completion”, -
https://arxiv.org/abs/2204.05999#facebook: “InCoder: A Generative Model for Code Infilling and Synthesis”, -
https://arxiv.org/abs/2204.02311#google: “PaLM: Scaling Language Modeling With Pathways”, -
2022-vaithilingam.pdf: “Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models”, Priyan Vaithilingam, Tianyi Zhang, Elena Glassman -
https://arxiv.org/abs/2201.10005#openai: “Text and Code Embeddings by Contrastive Pre-Training”, -
https://openai.com/research/webgpt: “WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing”, Jacob Hilton, Suchir Balaji, Reiichiro Nakano, John Schulman -
https://arxiv.org/abs/2112.09332#openai: “WebGPT: Browser-assisted Question-answering With Human Feedback”, -
https://arxiv.org/abs/2112.11446#deepmind: “Scaling Language Models: Methods, Analysis & Insights from Training Gopher”, -
https://arxiv.org/abs/2111.11904#microsoft: “Can Pre-trained Language Models Be Used to Resolve Textual and Semantic Merge Conflicts?”, Jialu Zhang, Todd Mytkowicz, Mike Kaufman, Ruzica Piskac, Shuvendu K. Lahiri -
https://arxiv.org/abs/2111.08267: “Solving Probability and Statistics Problems by Program Synthesis”, Leonard Tang, Elizabeth Ke, Nikhil Singh, Nakul Verma, Iddo Drori -
2021-jiang-2.pdf: “GenLine and GenForm: Two Tools for Interacting With Generative Language Models in a Code Editor”, Ellen Jiang, Edwin Toh, Alejandra Molina, Aaron Donsbach, Carrie Cai, Michael Terry