- See Also
-
Links
- “Foundational Challenges in Assuring Alignment and Safety of Large Language Models”, Anwar et al 2024
- “Training Dynamics of Contextual N-Grams in Language Models”, Quirke et al 2023
- “Explaining Grokking through Circuit Efficiency”, Varma et al 2023
- “A Theory for Emergence of Complex Skills in Language Models”, Arora & Goyal 2023
- “Teaching Arithmetic to Small Transformers”, Lee et al 2023
- “The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks”, Zhong et al 2023
- “Schema-Learning and Rebinding As Mechanisms of In-Context Learning and Emergence”, Swaminathan et al 2023
- “8 Things to Know about Large Language Models”, Bowman 2023
- “Toolformer: Language Models Can Teach Themselves to Use Tools”, Schick et al 2023
- “Interactive-Chain-Prompting (INTERCPT): Ambiguity Resolution for Crosslingual Conditional Generation With Interaction”, Pilault et al 2023
- “Progress Measures for Grokking via Mechanistic Interpretability”, Nanda et al 2023
- “Broken Neural Scaling Laws”, Caballero et al 2022
- “U-PaLM: Transcending Scaling Laws With 0.1% Extra Compute”, Tay et al 2022
- “Challenging BIG-Bench Tasks (BBH) and Whether Chain-Of-Thought Can Solve Them”, Suzgun et al 2022
- “Language Models Are Multilingual Chain-Of-Thought Reasoners”, Shi et al 2022
- “Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit”, Barak et al 2022
- “Emergent Abilities of Large Language Models”, Wei et al 2022
- “Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models”, Srivastava et al 2022
- “Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers”, Chan et al 2022
- “PaLM: Scaling Language Modeling With Pathways”, Chowdhery et al 2022
- “In-Context Learning and Induction Heads”, Olsson et al 2022
- “Predictability and Surprise in Large Generative Models”, Ganguli et al 2022
- “The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models”, Pan et al 2022
- “A Mathematical Framework for Transformer Circuits”, Elhage et al 2021
- “Scaling Language Models: Methods, Analysis & Insights from Training Gopher”, Rae et al 2021
- “A General Language Assistant As a Laboratory for Alignment”, Askell et al 2021
- “Mapping Language Models to Grounded Conceptual Spaces”, Patel & Pavlick 2021
- “Program Synthesis With Large Language Models”, Austin et al 2021
- “MMLU: Measuring Massive Multitask Language Understanding”, Hendrycks et al 2020
- “GPT-3: Language Models Are Few-Shot Learners”, Brown et al 2020
- “Emergence in Cognitive Science”, McClelland 2010
- “The Phase Transition In Human Cognition § Phase Transitions in Language Processing”, Spivey et al 2009 (page 13)
- “A Dynamic Systems Model of Cognitive and Language Growth”, Geert 1991
- Wikipedia
- Miscellaneous
- Link Bibliography
See Also
Links
“Foundational Challenges in Assuring Alignment and Safety of Large Language Models”, Anwar et al 2024
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
“Training Dynamics of Contextual N-Grams in Language Models”, Quirke et al 2023
“Explaining Grokking through Circuit Efficiency”, Varma et al 2023
“A Theory for Emergence of Complex Skills in Language Models”, Arora & Goyal 2023
“Teaching Arithmetic to Small Transformers”, Lee et al 2023
“The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks”, Zhong et al 2023
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
“Schema-Learning and Rebinding As Mechanisms of In-Context Learning and Emergence”, Swaminathan et al 2023
Schema-learning and rebinding as mechanisms of in-context learning and emergence
“8 Things to Know about Large Language Models”, Bowman 2023
“Toolformer: Language Models Can Teach Themselves to Use Tools”, Schick et al 2023
Toolformer: Language Models Can Teach Themselves to Use Tools
“Interactive-Chain-Prompting (INTERCPT): Ambiguity Resolution for Crosslingual Conditional Generation With Interaction”, Pilault et al 2023
“Progress Measures for Grokking via Mechanistic Interpretability”, Nanda et al 2023
Progress measures for grokking via mechanistic interpretability
“Broken Neural Scaling Laws”, Caballero et al 2022
“U-PaLM: Transcending Scaling Laws With 0.1% Extra Compute”, Tay et al 2022
“Challenging BIG-Bench Tasks (BBH) and Whether Chain-Of-Thought Can Solve Them”, Suzgun et al 2022
Challenging BIG-Bench Tasks (BBH) and Whether Chain-of-Thought Can Solve Them
“Language Models Are Multilingual Chain-Of-Thought Reasoners”, Shi et al 2022
“Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit”, Barak et al 2022
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit
“Emergent Abilities of Large Language Models”, Wei et al 2022
“Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models”, Srivastava et al 2022
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
“Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers”, Chan et al 2022
Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers
“PaLM: Scaling Language Modeling With Pathways”, Chowdhery et al 2022
“In-Context Learning and Induction Heads”, Olsson et al 2022
“Predictability and Surprise in Large Generative Models”, Ganguli et al 2022
“The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models”, Pan et al 2022
The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models
“A Mathematical Framework for Transformer Circuits”, Elhage et al 2021
“Scaling Language Models: Methods, Analysis & Insights from Training Gopher”, Rae et al 2021
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
“A General Language Assistant As a Laboratory for Alignment”, Askell et al 2021
“Mapping Language Models to Grounded Conceptual Spaces”, Patel & Pavlick 2021
“Program Synthesis With Large Language Models”, Austin et al 2021
“MMLU: Measuring Massive Multitask Language Understanding”, Hendrycks et al 2020
“GPT-3: Language Models Are Few-Shot Learners”, Brown et al 2020
“Emergence in Cognitive Science”, McClelland 2010
“The Phase Transition In Human Cognition § Phase Transitions in Language Processing”, Spivey et al 2009 (page 13)
The Phase Transition In Human Cognition § Phase Transitions in Language Processing
“A Dynamic Systems Model of Cognitive and Language Growth”, Geert 1991
Wikipedia
Miscellaneous
-
/doc/ai/scaling/emergence/2022-pan-figure2-largernnmodelsarebetteratrewardhacking.png: -
https://cse-robotics.engr.tamu.edu/dshell/cs689/papers/anderson72more_is_different.pdf -
https://www.lesswrong.com/s/5omSW4wNKbEvYsyje/p/GpSzShaaf8po4rcmA:View External Link:
https://www.lesswrong.com/s/5omSW4wNKbEvYsyje/p/GpSzShaaf8po4rcmA -
https://www.quantamagazine.org/the-unpredictable-abilities-emerging-from-large-ai-models-20230316/ -
https://www.reddit.com/r/mlscaling/comments/sjzvl0/d_instances_of_nonlog_capability_spikes_or/
Link Bibliography
-
https://arxiv.org/abs/2307.15936: “A Theory for Emergence of Complex Skills in Language Models”, -
https://arxiv.org/abs/2307.03381: “Teaching Arithmetic to Small Transformers”, -
https://arxiv.org/abs/2307.01201#deepmind: “Schema-Learning and Rebinding As Mechanisms of In-Context Learning and Emergence”, -
https://arxiv.org/abs/2210.11399#google: “U-PaLM: Transcending Scaling Laws With 0.1% Extra Compute”, -
https://arxiv.org/abs/2210.09261#google: “Challenging BIG-Bench Tasks (BBH) and Whether Chain-Of-Thought Can Solve Them”, -
https://arxiv.org/abs/2210.03057#google: “Language Models Are Multilingual Chain-Of-Thought Reasoners”, -
https://arxiv.org/abs/2206.04615: “Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models”, -
https://arxiv.org/abs/2204.02311#google: “PaLM: Scaling Language Modeling With Pathways”, -
https://arxiv.org/abs/2202.07785#anthropic: “Predictability and Surprise in Large Generative Models”, -
https://arxiv.org/abs/2201.03544: “The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models”, -
https://arxiv.org/abs/2112.11446#deepmind: “Scaling Language Models: Methods, Analysis & Insights from Training Gopher”, -
https://arxiv.org/abs/2112.00861#anthropic: “A General Language Assistant As a Laboratory for Alignment”, -
https://openreview.net/forum?id=gJcEM8sxHK: “Mapping Language Models to Grounded Conceptual Spaces”, -
https://arxiv.org/abs/2009.03300: “MMLU: Measuring Massive Multitask Language Understanding”, -
https://onlinelibrary.wiley.com/doi/full/10.1111/j.1756-8765.2010.01116.x: “Emergence in Cognitive Science”, -
1991-vangeert.pdf: “A Dynamic Systems Model of Cognitive and Language Growth”,