“‘PaLM 2’ Tag”,2023-02-16

Bibliography for tag ai/nn/transformer/gpt/palm/2, most recent first: 28 annotations & 2 links (parent).

• See Also
• Links
  • “Alphabet Q3 Earnings Call: CEO Sundar Pichai’s Remarks”
  • “Scalable Watermarking for Identifying Large Language Model Outputs”
  • “Inference Scaling for Long-Context Retrieval Augmented Generation”, Yue et al 2024
  • “Project Zero: From Naptime to Big Sleep: Using Large Language Models To Catch Vulnerabilities In Real-World Code”
  • “Training Language Models to Self-Correct via Reinforcement Learning”, Kumar et al 2024
  • “On Scalable Oversight With Weak LLMs Judging Strong LLMs”, Kenton et al 2024
  • “Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?”, Lee et al 2024
  • “What Are the Odds? Language Models Are Capable of Probabilistic Reasoning”, Paruchuri et al 2024
  • “Can Language Models Use Forecasting Strategies?”, Pratt et al 2024
  • “Grokked Transformers Are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization”, Wang et al 2024
  • “Many-Shot In-Context Learning”, Agarwal et al 2024
  • “Few-Shot Recalibration of Language Models”, Li et al 2024
  • “Long-Form Factuality in Large Language Models”, Wei et al 2024
  • “When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method”, Zhang et al 2024
  • “ReST Meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent”, Aksitov et al 2023
  • “Rich Human Feedback for Text-To-Image Generation”, Liang et al 2023
  • “Beyond Human Data: Scaling Self-Training for Problem-Solving With Language Models (ReST^EM)”, Singh et al 2023
  • “Universal Self-Consistency for Large Language Model Generation”, Chen et al 2023
  • “Instruction-Following Evaluation for Large Language Models”, Zhou et al 2023
  • “A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models”, Eisape et al 2023
  • “PAIR: Jailbreaking Black Box Large Language Models in 20 Queries”, Chao et al 2023
  • “RLAIF: Scaling Reinforcement Learning from Human Feedback With AI Feedback”, Lee et al 2023
  • “Android in the Wild: A Large-Scale Dataset for Android Device Control”, Rawles et al 2023
  • “Google’s Newest AI Model Uses Nearly 5× More Text Data for Training Than Its Predecessor”, Elias2023
  • “Pretraining Language Models With Human Preferences”, Korbak et al 2023
  • “Working With AI (Part 2): Code Conversion”
  • “How Good Are LLMs at Doing ML on an Unknown Dataset?”
  • “What Happened to BERT & T5? On Transformer Encoders, PrefixLM and Denoising Objectives”, Tay2024
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    • language-self-correction
    • self-training
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• Bibliography