“‘Codex’ Tag”,2019-12-25 (instruct-tuning LLMs, AI economics)⁠:

Bibliography for tag ai/nn/transformer/gpt/codex, most recent first: 175 annotations & 287 links (parent).

• See Also
• Gwern
  • “latex2unicode.py”, Gwern2023
  • “CQK Is The First Unused TLA”, Gwern2023
• Links
  • “They All Use It”, Ball2024
  • “Business Spending on AI Surged 500% This Year to $13.8 Billion”
  • “Alphabet Q3 Earnings Call: CEO Sundar Pichai’s Remarks”
  • “Hacking Back the AI-Hacker: Prompt Injection As a Defense Against LLM-Driven Cyberattacks”, Pasquini et al 2024
  • “MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering”, Chan et al 2024
  • “Project Zero: From Naptime to Big Sleep: Using Large Language Models To Catch Vulnerabilities In Real-World Code”
  • “Evaluation of OpenAI O1: Opportunities and Challenges of AGI”, Zhong et al 2024
  • “Language Models Learn to Mislead Humans via RLHF”, Wen et al 2024
  • “Using ChatGPT to Reverse Engineer Minified JavaScript”
  • “SWE-Bench Technical Report: 22%”, Honeycomb2024
  • “AI-Powered Coding Pulls in Almost $1bn of Funding to Claim ‘Killer App’ Status”, Murgia2024
  • “Prompt Injection in ‘Resolve Vulnerabilty’ Results in Arbitrary Command Execution in Victim’s Pipeline”, GitLab2024
  • “To Code, or Not To Code? Exploring Impact of Code in Pre-Training”, Aryabumi et al 2024
  • “Replacing My Right Hand With AI”, Schluntz2024
  • “APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets”, Liu et al 2024
  • “Diffusion On Syntax Trees For Program Synthesis”, Kapur et al 2024
  • “A Peter Thiel-Backed AI Startup, Cognition Labs, Seeks $2 Billion Valuation: Funding round Could Increase Startup’s Valuation Nearly Sixfold in a Matter of Weeks, Reflecting AI Frenzy”, Jin2024
  • “Vulnerability Detection With Code Language Models: How Far Are We?”, Ding et al 2024
  • “Gold-Medalist Coders Build an AI That Can Do Their Job for Them: A New Startup Called Cognition AI Can Turn a User’s Prompt into a Website or Video Game”, Vance2024
  • “TestGen-LLM: Automated Unit Test Improvement Using Large Language Models at Meta”, Alshahwan et al 2024
  • “The Impact of AI Tool on Engineering at ANZ Bank: An Empirical Study on GitHub Copilot Within a Corporate Environment”, Chatterjee et al 2024
  • “CodeIt: Self-Improving Language Models With Prioritized Hindsight Replay”, Butt et al 2024
  • “Coding on Copilot: 2023 Data Shows Downward Pressure on Code Quality, Plus Projections for 2024”, Harding & Kloster2024
  • “Sleeper Agents: Training Deceptive LLMs That Persist Through Safety Training”, Hubinger et al 2024
  • “Leveraging Large Language Models to Boost Dafny’s Developers Productivity”, Silva et al 2024
  • “WaveCoder: Widespread And Versatile Enhanced Instruction Tuning With Refined Data Generation”, Yu et al 2023
  • “StarVector: Generating Scalable Vector Graphics Code from Images”, Rodriguez et al 2023
  • “Universal Self-Consistency for Large Language Model Generation”, Chen et al 2023
  • “LLM-Assisted Code Cleaning For Training Accurate Code Generators”, Jain et al 2023
  • “A Coder Considers the Waning Days of the Craft: Coding Has Always Felt to Me like an Endlessly Deep and Rich Domain. Now I Find Myself Wanting to Write a Eulogy for It”, Somers2023
  • “ChipNeMo: Domain-Adapted LLMs for Chip Design”, Liu et al 2023
  • “CodeFusion: A Pre-Trained Diffusion Model for Code Generation”, Singh et al 2023
  • “Eureka: Human-Level Reward Design via Coding Large Language Models”, Ma et al 2023
  • “Data Contamination Through the Lens of Time”, Roberts et al 2023
  • “SWE-Bench: Can Language Models Resolve Real-World GitHub Issues?”, Jimenez et al 2023
  • “Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models”, Zhou et al 2023
  • “PassUntil: Predicting Emergent Abilities With Infinite Resolution Evaluation”, Hu et al 2023
  • “Security Weaknesses of Copilot Generated Code in GitHub”, Fu et al 2023
  • “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 & Aaronson2023
  • “Insights into Stack Overflow’s Traffic: We’re Setting the Record Straight”, Darilek2023
  • “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
  • “InterCode: Standardizing and Benchmarking Interactive Coding With Execution Feedback”, Yang et al 2023
  • “AI Is a Lot of Work: As the Technology Becomes Ubiquitous, a Vast Tasker Underclass Is Emerging—And Not Going Anywhere”, Dzieza2023
  • “When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming (CDHF)”, Mozannar et al 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
  • “StarCoder: May the Source Be With You!”, Li 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
  • “Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes”, Arora et al 2023
  • “How Secure Is Code Generated by ChatGPT?”, Khoury 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”, Tao2023
  • “Language Models Can Solve Computer Tasks”, Kim et al 2023
  • “Introducing Microsoft 365 Copilot—Your Copilot for Work”, Spataro2023
  • “Reflexion: Language Agents With Verbal Reinforcement Learning”, Shinn et al 2023
  • “Large Language Models and Simple, Stupid Bugs”, Jesse et al 2023
  • “Larger Language Models Do In-Context Learning Differently”, Wei 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
  • “Faithful Chain-Of-Thought Reasoning”, Lyu et al 2023
  • “Large Language Models Are Versatile Decomposers: Decompose Evidence and Questions for Table-Based Reasoning”, Ye et al 2023
  • “Google Is Asking Employees to Test Potential ChatGPT Competitors, including a Chatbot Called ‘Apprentice Bard’”, Elias2023
  • “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”, Leahy2023
  • “General Availability of Azure OpenAI Service Expands Access to Large, Advanced AI Models With Added Enterprise Benefits”, Boyd2023
  • “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
  • “Do Users Write More Insecure Code With AI Assistants?”, Perry et al 2022
  • “Broken Neural Scaling Laws”, Caballero et al 2022
  • “Programming Possibility: Kevin Scott on AI’s Impact on Cognitive Work”, Hoffman & Scott2022
  • “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
  • “Limitations of Language Models in Arithmetic and Symbolic Induction”, Qian et al 2022
  • “Language Models Can Teach Themselves to Program Better”, Haluptzok 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 & Neubig2022
  • “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 & Raff2022
  • “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
  • “Few-Shot Semantic Parsing With Language Models Trained On Code”, Shin & Durme2021
  • “WebGPT: Browser-Assisted Question-Answering With Human Feedback”, Nakano et al 2021
  • “WebGPT: Improving the Factual Accuracy of Language Models through Web Browsing”, Hilton et al 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 Linear Algebra by Program Synthesis”, Drori & Verma2021
  • “Solving Probability and Statistics Problems by Program Synthesis”, Tang et al 2021
  • “Automatic Program Repair With OpenAI’s Codex: Evaluating QuixBugs”, Prenner & Robbes2021
  • “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’Boyle2021
  • “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”, Ziegler2021
  • “Microsoft and OpenAI Have a New AI Tool That Will Give Coding Suggestions to Software Developers”, Novet2021
  • “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
  • “IJON: Exploring Deep State Spaces via Fuzzing”, Aschermann et al 2020
  • “IntelliCode Compose: Code Generation Using Transformer”, Svyatkovskiy et al 2020
  • “Deep Learning for Symbolic Mathematics”, Lample & Charton2019
  • “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 & Freitas2015
  • “Computers Doing The Right Thing”
  • “OpenAI API Alchemy: Smart Formatting and Code Creation”
  • “Building Games and Apps Entirely through Natural Language Using OpenAI’s Code-Davinci Model”
  • “Replit”
  • “Working With AI (Part 2): Code Conversion”
  • “An Amazing Journey With Claude 3.5 and ChatGPT-4o Who Helped Me Backwards Engineer an Econometrics Theory Paper and Taught Me a Lot More in the Process”
  • “StenographyDev/autopilot-Vsc”
  • “Copilot Stops Working on `gender` Related Subjects · Community · Discussion #72603”
  • “Revolutionize Your Project Documentation With the Codex-README Generator, Utilizing OpenAI’s Codex for Intelligent README Creation.”
  • “LLM Powered Autonomous Agents”
  • “The RetroInstruct Guide To Synthetic Text Data”, Pressman2024
  • “Fun and Dystopia With AI-Based Code Generation Using GPT-J-6B”
  • “There’s a Running Theme in Here of Programming Problems LLMs Solve Where It’s…”
  • “How Anthropic Built Artifacts”, Orosz2024
  • “How I Use ‘AI’”, Carlini2024
  • “Using GPT-3 to Explain How Code Works”
  • “Adept Video Demo!”
  • “Transformer-VAE for Program Synthesis”
  • “Writer”
  • “Introducing ‘Computer Use’, a New Claude 3.5 Sonnet, and Claude 3.5 Haiku”, Anthropic2024
  • “Claude 3.5 Sonnet on GitHub Copilot”
  • “Developing a Computer Use Model”, Anthropic2024
  • “Websim, Worldsim, and The Summer of Simulative AI”
  • “I Found >800 Orthogonal ‘Write Code’ Steering Vectors”
  • “Who Models the Models That Model Models? An Exploration of GPT-3’s In-Context Model Fitting Ability”
  • “OpenAI Codex: First Impressions”
  • “A.I. Can Now Write Its Own Computer Code. That’s Good News for Humans.”
  • “Balloons! The Balloon Clicker Game”
  • “Tabnine AI Code Assistant”
  • “OpenAI Can Translate English into Code With Its New Machine Learning Software Codex”
  • “FROM PLAIN TO EXPLAINED IN FIVE MINUTES: Getting Started With Stenography Autopilot”
  • “OpenAI Codex Live Demo”
  • “Is Finetuning GPT-4o worth It?”
  • “Creating a Space Game With OpenAI Codex”
  • sharifshameem
  • sharifshameem
  • spolu
  • “XBOW Now Matches the Capabilities of a Top Human Pentester”, XBOW 2024
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    • code-synthesis
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