- See Also
-
Links
- “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
- “Grokked Transformers Are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization”, Wang et al 2024
- “Few-Shot Recalibration of Language Models”, Li 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 (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
- Sort By Magic
- Miscellaneous
- Link Bibliography
See Also
Links
“Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?”, Lee et al 2024
Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?
“What Are the Odds? Language Models Are Capable of Probabilistic Reasoning”, Paruchuri et al 2024
What Are the Odds? Language Models Are Capable of Probabilistic Reasoning
“Grokked Transformers Are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization”, Wang et al 2024
Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization
“Few-Shot Recalibration of Language Models”, Li et al 2024
“When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method”, Zhang et al 2024
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
“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
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.
finetuning
self-training
language-models
Miscellaneous
Link Bibliography
-
https://arxiv.org/abs/2406.13121#google
: “Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?”, -
https://arxiv.org/abs/2405.15071
: “Grokked Transformers Are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization”, -
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”, -
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”,