- See Also
-
Links
- “Few-Shot Recalibration of Language Models”, Li 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 (ReSTEM)”, 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
- “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”, Elias 2023
- “Pretraining Language Models With Human Preferences”, Korbak et al 2023
- Miscellaneous
- Link Bibliography
See Also
Links
“Few-Shot Recalibration of Language Models”, Li et al 2024
“ReST Meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent”, Aksitov et al 2023
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent
“Rich Human Feedback for Text-To-Image Generation”, Liang et al 2023
“Beyond Human Data: Scaling Self-Training for Problem-Solving With Language Models (ReSTEM)”, Singh et al 2023
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models (ReSTEM)
“Universal Self-Consistency for Large Language Model Generation”, Chen et al 2023
Universal Self-Consistency for Large Language Model Generation
“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
A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models
“PAIR: Jailbreaking Black Box Large Language Models in 20 Queries”, Chao et al 2023
PAIR: Jailbreaking Black Box Large Language Models in 20 Queries
“Android in the Wild: A Large-Scale Dataset for Android Device Control”, Rawles et al 2023
Android in the Wild: A Large-Scale Dataset for Android Device Control
“Google’s Newest AI Model Uses Nearly 5× More Text Data for Training Than Its Predecessor”, Elias 2023
Google’s newest AI model uses nearly 5× more text data for training than its predecessor
“Pretraining Language Models With Human Preferences”, Korbak et al 2023
Miscellaneous
Link Bibliography
-
https://arxiv.org/abs/2312.06585#deepmind: “Beyond Human Data: Scaling Self-Training for Problem-Solving With Language Models (ReSTEM)”, -
https://arxiv.org/abs/2310.08419: “PAIR: Jailbreaking Black Box Large Language Models in 20 Queries”, Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, Eric Wong -
https://www.cnbc.com/2023/05/16/googles-palm-2-uses-nearly-five-times-more-text-data-than-predecessor.html: “Google’s Newest AI Model Uses Nearly 5× More Text Data for Training Than Its Predecessor”, Jennifer Elias