Bibliography:

  1. ‘PaLM’ tag

  2. Alphabet Q3 Earnings Call: CEO Sundar Pichai’s Remarks

  3. Scalable Watermarking for Identifying Large Language Model Outputs

  4. Inference Scaling for Long-Context Retrieval Augmented Generation

  5. Project Zero: From Naptime to Big Sleep: Using Large Language Models To Catch Vulnerabilities In Real-World Code

  6. Training Language Models to Self-Correct via Reinforcement Learning

  7. On scalable oversight with weak LLMs judging strong LLMs

  8. Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?

  9. What Are the Odds? Language Models Are Capable of Probabilistic Reasoning

  10. Can Language Models Use Forecasting Strategies?

  11. Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization

  12. Many-Shot In-Context Learning

  13. Few-Shot Recalibration of Language Models

  14. Long-form factuality in large language models

  15. When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

  16. ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent

  17. Rich Human Feedback for Text-to-Image Generation

  18. Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models (ReSTEM)

  19. Universal Self-Consistency for Large Language Model Generation

  20. Instruction-Following Evaluation for Large Language Models

  21. A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models

  22. PAIR: Jailbreaking Black Box Large Language Models in 20 Queries

  23. RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

  24. Android in the Wild: A Large-Scale Dataset for Android Device Control

  25. Google’s newest AI model uses nearly 5× more text data for training than its predecessor

  26. Pretraining Language Models with Human Preferences

  27. Working With AI (Part 2): Code Conversion

  28. fd94ca950274977e4321f54a45033143e8b87efc.html

  29. How Good Are LLMs at Doing ML on an Unknown Dataset?

  30. What Happened to BERT & T5? On Transformer Encoders, PrefixLM and Denoising Objectives

  31. 9070c645ad4e38c52aceee032690150a0ca89c74.html

  32. design#future-tag-features

    [Transclude the forward-link's context]

  33. https://blog.google/technology/ai/google-palm-2-ai-large-language-model/

  34. https://x.com/andrew_n_carr/status/1857262016106520655

  35. Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?

  36. https%253A%252F%252Farxiv.org%252Fabs%252F2406.13121%2523google.html

  37. Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization

  38. https%253A%252F%252Farxiv.org%252Fabs%252F2405.15071.html

  39. Long-form factuality in large language models

  40. https%253A%252F%252Farxiv.org%252Fabs%252F2403.18802%2523deepmind.html

  41. Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models (ReSTEM)

  42. Abhishek Kumar

  43. Igor Mordatch

  44. Behnam Neyshabur

  45. Jascha Sohl-Dickstein

  46. https%253A%252F%252Farxiv.org%252Fabs%252F2312.06585%2523deepmind.html

  47. PAIR: Jailbreaking Black Box Large Language Models in 20 Queries

  48. https%253A%252F%252Farxiv.org%252Fabs%252F2310.08419.html

  49. Google’s newest AI model uses nearly 5× more text data for training than its predecessor

  50. https%253A%252F%252Fwww.cnbc.com%252F2023%252F05%252F16%252Fgoogles-palm-2-uses-nearly-five-times-more-text-data-than-predecessor.html.html