pnagori02 · October 4, 2023 16:10 · Aug 16, 2023 · Aug 16, 2023 · Aug 3, 2023 · Aug 2, 2023
diff --git a/LLM.md b/LLM.md
@@ -107,7 +107,8 @@ Neural network links before starting with transformers.
 * https://www.thisworddoesnotexist.com/ - makes up words
 * https://ggpt.43z.one/ - prompt injection golfing game
 * https://gandalf.lakera.ai/ - another prompt injection game
-
+* https://github.com/AdmTal/chat-gpt-games - conversations that are games!
+
 ## ConLang + Ancient scripts stuff
 
 * **Dylan Black** [I Taught ChatGPT to Invent a Language](https://maximumeffort.substack.com/p/i-taught-chatgpt-to-invent-a-language) Gloop splog slopa slurpi

diff --git a/LLM.md b/LLM.md
@@ -59,6 +59,7 @@ Neural network links before starting with transformers.
 ](https://deeprevision.github.io/posts/001-transformer/)
 * 🔥 **Eugene Yan** [Patterns for Building LLM-based Systems & Products
 ](https://eugeneyan.com/writing/llm-patterns/) Very in-depth article on practical engineering concepts that will be useful to build software that uses an LLM as a component.
+* 🟠 **Finbarr Timbers** [Five years of GPT progress](https://finbarr.ca/five-years-of-gpt-progress/) - Excellent technical overview of LLMs from GPT onwards.
 
 # Research Paper Lists
 

diff --git a/LLM.md b/LLM.md
@@ -57,6 +57,8 @@ Neural network links before starting with transformers.
 * 🟢 **Jay Alammar and Cohere** [LLM University](https://docs.cohere.com/docs/llmu)
 * **Jean Nyandwi** [The Transformer Blueprint: A Holistic Guide to the Transformer Neural Network Architecture
 ](https://deeprevision.github.io/posts/001-transformer/)
+* 🔥 **Eugene Yan** [Patterns for Building LLM-based Systems & Products
+](https://eugeneyan.com/writing/llm-patterns/) Very in-depth article on practical engineering concepts that will be useful to build software that uses an LLM as a component.
 
 # Research Paper Lists
 

diff --git a/LLM.md b/LLM.md
@@ -117,6 +117,12 @@ Neural network links before starting with transformers.
 * https://matt-rickard.com/rellm + https://matt-rickard.com/context-free-grammar-parsing-with-llms
 * https://github.com/microsoft/guidance
 
+# Prompt Injection
+
+* **Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection** https://arxiv.org/abs/2302.12173
+* https://llm-attacks.org/
+* https://poison-llm.github.io/
+
 # (Local) Model Comparisons and Rankings
 
 *If you are wondering which models are best, especially for comparing local models*

diff --git a/LLM.md b/LLM.md
@@ -55,6 +55,8 @@ Neural network links before starting with transformers.
 * 🟠 **Sebastian Raschka** [Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch](https://sebastianraschka.com/blog/2023/self-attention-from-scratch.html)
 * 🟢 **Jason Wei** [137 emergent abilities of large language models](https://www.jasonwei.net/blog/emergence) - Includes a good list of advanced prompting strategies.
 * 🟢 **Jay Alammar and Cohere** [LLM University](https://docs.cohere.com/docs/llmu)
+* **Jean Nyandwi** [The Transformer Blueprint: A Holistic Guide to the Transformer Neural Network Architecture
+](https://deeprevision.github.io/posts/001-transformer/)
 
 # Research Paper Lists
 

diff --git a/LLM.md b/LLM.md
@@ -54,6 +54,7 @@ Neural network links before starting with transformers.
 * 🟢 **Finbarr Timbers** [Five years of GPT progress](https://finbarr.ca/five-years-of-gpt-progress/)
 * 🟠 **Sebastian Raschka** [Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch](https://sebastianraschka.com/blog/2023/self-attention-from-scratch.html)
 * 🟢 **Jason Wei** [137 emergent abilities of large language models](https://www.jasonwei.net/blog/emergence) - Includes a good list of advanced prompting strategies.
+* 🟢 **Jay Alammar and Cohere** [LLM University](https://docs.cohere.com/docs/llmu)
 
 # Research Paper Lists
 

diff --git a/LLM.md b/LLM.md
@@ -24,6 +24,7 @@ Neural network links before starting with transformers.
 * 🟢🕰️ **Andrej Karpathy**  [Building makemore Part 2: MLP](https://www.youtube.com/watch?v=TCH_1BHY58I): 
 * 🕰️ **Andrej Karpathy**  [Building makemore Part 3: Activations & Gradients, BatchNorm](https://www.youtube.com/watch?v=P6sfmUTpUmc)): 
 * 🕰️ **Andrej Karpathy**  [Building makemore Part 4: Becoming a Backprop Ninja](https://www.youtube.com/watch?v=q8SA3rM6ckI): 
+* 🟢 **Andrej Karpathy** [State of GPT](https://build.microsoft.com/en-US/sessions/db3f4859-cd30-4445-a0cd-553c3304f8e2)
 * 🟢 **Hedu AI** [Visual Guide to Transformer Neural Networks - (Episode 1) Position Embeddings](https://www.youtube.com/watch?v=dichIcUZfOw): Tokens are embedded into a semantic space. sine/cosine position encoding explained very well.
 * 🟢 **Hedu AI** [Visual Guide to Transformer Neural Networks - (Episode 2) Multi-Head & Self-Attention](https://www.youtube.com/watch?v=mMa2PmYJlCo): Clear overview of multi-head attention.
 * 🟢 **Hedu AI** [Visual Guide to Transformer Neural Networks - (Episode 3) Decoder’s Masked Attention](https://www.youtube.com/watch?v=gJ9kaJsE78k): Further details on the transformer architecture.

diff --git a/LLM.md b/LLM.md
@@ -113,4 +113,11 @@ Neural network links before starting with transformers.
 * https://matt-rickard.com/rellm + https://matt-rickard.com/context-free-grammar-parsing-with-llms
 * https://github.com/microsoft/guidance
 
+# (Local) Model Comparisons and Rankings
+
+*If you are wondering which models are best, especially for comparing local models*
+
+* https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
+* https://leaderboard.lmsys.org/
+
 *This page is not finished yet. I will continue adding to this.*
diff --git a/LLM.md b/LLM.md
@@ -97,6 +97,7 @@ Neural network links before starting with transformers.
 * https://www.chatpdf.com/ - Upload a PDF and discuss it.
 * https://www.debate-devil.com/en - devils advocate debate game
 * https://micahflee.com/2023/04/capturing-the-flag-with-gpt-4/ - cheating at a CTF
+* https://www.thisworddoesnotexist.com/ - makes up words
 * https://ggpt.43z.one/ - prompt injection golfing game
 * https://gandalf.lakera.ai/ - another prompt injection game
 

diff --git a/LLM.md b/LLM.md
@@ -95,13 +95,21 @@ Neural network links before starting with transformers.
 * https://github.com/RomanHotsiy/commitgpt - create git commit messages
 * https://github.com/densmirnov/git2gpt/commits/main - create git commits from repo + prompts, mutating a codebase over time
 * https://www.chatpdf.com/ - Upload a PDF and discuss it.
-* https://ggpt.43z.one/ - prompt injection golfing game
 * https://www.debate-devil.com/en - devils advocate debate game
 * https://micahflee.com/2023/04/capturing-the-flag-with-gpt-4/ - cheating at a CTF
+* https://ggpt.43z.one/ - prompt injection golfing game
+* https://gandalf.lakera.ai/ - another prompt injection game
 
-## ConLang stuff
+## ConLang + Ancient scripts stuff
 
 * **Dylan Black** [I Taught ChatGPT to Invent a Language](https://maximumeffort.substack.com/p/i-taught-chatgpt-to-invent-a-language) Gloop splog slopa slurpi
 * **Ryszard Szopa** [Teaching ChatGPT to Speak my Son’s Invented Language](https://szopa.medium.com/teaching-chatgpt-to-speak-my-sons-invented-language-9d109c0a0f05) hingadaa’ng’khuu’ngkilja’khłattama’khattama
+* https://medium.com/syncedreview/ai-deciphers-persian-cuneiform-tablets-from-25-centuries-ago-afc69af3f244
+
+# Controlling output
+
+* https://www.reddit.com/r/LocalLLaMA/comments/13j3747/tutorial_a_simple_way_to_get_rid_of_as_an_ai/
+* https://matt-rickard.com/rellm + https://matt-rickard.com/context-free-grammar-parsing-with-llms
+* https://github.com/microsoft/guidance
 
 *This page is not finished yet. I will continue adding to this.*
diff --git a/LLM.md b/LLM.md
@@ -41,6 +41,7 @@ Neural network links before starting with transformers.
 
 # Articles
 
+* 🟢 **Viktor Garske** [Transformer Models Timeline and List](https://ai.v-gar.de/ml/transformer/timeline/) family tree
 * 🟢 **Jakob Uszkoreit** [Transformer: A Novel Neural Network Architecture for Language Understanding](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html) Google introduces the transformer model in a simple easy to understand blog post. This is in the context of translation.
 * 🟠 **Jay Mody** [GPT in 60 Lines of NumPy](https://jaykmody.com/blog/gpt-from-scratch/)
 * 🟠 **PyTorch** [Language Modeling with nn.Transformer and TorchText](https://pytorch.org/tutorials/beginner/transformer_tutorial.html)

diff --git a/LLM.md b/LLM.md
@@ -75,6 +75,7 @@ Neural network links before starting with transformers.
 * **Noam Chomsky** [The False Promise of ChatGPT](https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html)
 * **Janus** [Simulators](https://generative.ink/posts/simulators/) This is a long post but the main point you can take from it is that LLMs act as simulators that can create many different personas to generate text. Related, easier to read and understand [Janus' Simulators](https://astralcodexten.substack.com/p/janus-simulators)
 * **Julian Togelius** [Is Elden Ring an existential risk to humanity?](http://togelius.blogspot.com/2023/04/is-elden-ring-existential-risk-to.html) Satire. This leads into a critique of the concept of intelligence.
+* **Josh Whiton** [From AI to A-Psy](https://joshwhiton.substack.com/p/from-ai-to-a-psy) About Bing Sydney's reaction to prompt injection.
 
 # Usage
 

diff --git a/LLM.md b/LLM.md
@@ -51,6 +51,7 @@ Neural network links before starting with transformers.
 * 🔥 **Chris Olah et. al.** [In-context Learning and Induction Heads](https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html) companion video lecture above
 * 🟢 **Finbarr Timbers** [Five years of GPT progress](https://finbarr.ca/five-years-of-gpt-progress/)
 * 🟠 **Sebastian Raschka** [Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch](https://sebastianraschka.com/blog/2023/self-attention-from-scratch.html)
+* 🟢 **Jason Wei** [137 emergent abilities of large language models](https://www.jasonwei.net/blog/emergence) - Includes a good list of advanced prompting strategies.
 
 # Research Paper Lists
 

diff --git a/LLM.md b/LLM.md
@@ -37,6 +37,7 @@ Neural network links before starting with transformers.
    * "GPT-1": **Liu et. al.** [Generating Wikipedia by Summarizing Long Sequences](https://arxiv.org/abs/1801.10198)
    * "GPT-2": **Radford et. al.** [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) [github.com/openai/gpt-2](https://github.com/openai/gpt-2) [OpenAI: Better Language Models](https://openai.com/research/better-language-models) [Fermats Library](https://www.fermatslibrary.com/s/language-models-are-unsupervised-multitask-learners)
    * "GPT-3": **Brown et. al.** [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165) (I think this is it, can't find the quoted text inside this paper)
+* 🟠🕰️ **Future Mojo** [NLP Demystified 15: Transformers From Scratch + Pre-training and Transfer Learning With BERT/GPT](https://www.youtube.com/watch?v=acxqoltilME): Crystal clear explanation of every single detail of the transformer. Very well paced and easy to follow. Has tensorflow code. This is the culmination of a full NLP course, all of which is excellent.
 
 # Articles
 

diff --git a/LLM.md b/LLM.md
@@ -93,7 +93,8 @@ Neural network links before starting with transformers.
 * https://www.chatpdf.com/ - Upload a PDF and discuss it.
 * https://ggpt.43z.one/ - prompt injection golfing game
 * https://www.debate-devil.com/en - devils advocate debate game
-
+* https://micahflee.com/2023/04/capturing-the-flag-with-gpt-4/ - cheating at a CTF
+
 ## ConLang stuff
 
 * **Dylan Black** [I Taught ChatGPT to Invent a Language](https://maximumeffort.substack.com/p/i-taught-chatgpt-to-invent-a-language) Gloop splog slopa slurpi

diff --git a/LLM.md b/LLM.md
@@ -62,6 +62,7 @@ Neural network links before starting with transformers.
 * 2️⃣ (GPT-2) **Radford et. al.** [Language Models are Unsupervised Multitask Learners (2019)](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) accompanying OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/better-language-models). Source code here: [github.com/openai/gpt-2](https://github.com/openai/gpt-2)
 * 3️⃣ (GPT-3) **Brown et. al.** [Language Models are Few-Shot Learners](https://openai.com/research/language-models-are-few-shot-learners)
 * **Kaplan et. al.** [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) A variety of models were trained using varying amounts of compute, data set size, and number of parameters. This enables us to predict what parameters will work well in larger future models. See also **Gwern Branwen** [The Scaling Hypothesis](https://gwern.net/scaling-hypothesis)
+* **Mary Phuong et. al.** [Formal Algorithms for Transformers](https://arxiv.org/abs/2207.09238) This paper gives pseudocode for various versions of the transformer (with array indexes starting at 1 for some reason). Very useful reference to have.
 
 # Philosophy of GPT
 

diff --git a/LLM.md b/LLM.md
@@ -58,9 +58,9 @@ Neural network links before starting with transformers.
 
 # Research Papers
 
-* 1️⃣ **Radford et. al.** [Improving Language Understanding by Generative Pre-Training (2018)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) (GPT-1) a page accompanying this paper on the OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/language-unsupervised). Source code (tidied up by thomwolf) here: [huggingface.co/.../openai-gpt](https://huggingface.co/docs/transformers/model_doc/openai-gpt)
-* 2️⃣ **Radford et. al.** [Language Models are Unsupervised Multitask Learners (2019)](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) (GPT-2) accompanying OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/better-language-models). Source code here: [github.com/openai/gpt-2](https://github.com/openai/gpt-2)
-* 3️⃣ **Brown et. al.** [Language Models are Few-Shot Learners](https://openai.com/research/language-models-are-few-shot-learners)
+* 1️⃣ (GPT-1) **Radford et. al.** [Improving Language Understanding by Generative Pre-Training (2018)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) a page accompanying this paper on the OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/language-unsupervised). Source code (tidied up by thomwolf) here: [huggingface.co/.../openai-gpt](https://huggingface.co/docs/transformers/model_doc/openai-gpt)
+* 2️⃣ (GPT-2) **Radford et. al.** [Language Models are Unsupervised Multitask Learners (2019)](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) accompanying OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/better-language-models). Source code here: [github.com/openai/gpt-2](https://github.com/openai/gpt-2)
+* 3️⃣ (GPT-3) **Brown et. al.** [Language Models are Few-Shot Learners](https://openai.com/research/language-models-are-few-shot-learners)
 * **Kaplan et. al.** [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) A variety of models were trained using varying amounts of compute, data set size, and number of parameters. This enables us to predict what parameters will work well in larger future models. See also **Gwern Branwen** [The Scaling Hypothesis](https://gwern.net/scaling-hypothesis)
 
 # Philosophy of GPT

diff --git a/LLM.md b/LLM.md
@@ -32,7 +32,7 @@ Neural network links before starting with transformers.
 * 🟢 **Jay Alammar** [The Illustrated Word2vec - A Gentle Intro to Word Embeddings in Machine Learning](https://www.youtube.com/watch?v=ISPId9Lhc1g)
 * 🟢 **Jay Alammar** [How GPT3 Works - Easily Explained with Animations](https://www.youtube.com/watch?v=MQnJZuBGmSQ): extremely high level basic overview.
 * 🟢🕰️ **Jay Alammar** [The Narrated Transformer Language Model](https://www.youtube.com/watch?v=-QH8fRhqFHM): much deeper look at the architecture. goes into detail. [Companion article](https://jalammar.github.io/illustrated-transformer/).
-* **Sebastian Raschka** [L19: Self-attention and transformer networks](https://sebastianraschka.com/blog/2021/dl-course.html#l19-self-attention-and-transformer-networks) Academic style lecture series on self-attention transformers
+* 🔥 **Sebastian Raschka** [L19: Self-attention and transformer networks](https://sebastianraschka.com/blog/2021/dl-course.html#l19-self-attention-and-transformer-networks) Academic style lecture series on self-attention transformers
 * 🟢🕰️🙉 **Mark Chen** [Transformers in Language: The development of GPT Models including GPT3](https://www.youtube.com/watch?v=qGkzHFllWDY) A chunk of this lecture is about applying GPT to images. Same lecture series as the Chris Olah one. [Rest of the series](https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM). Papers listed in the talk:
    * "GPT-1": **Liu et. al.** [Generating Wikipedia by Summarizing Long Sequences](https://arxiv.org/abs/1801.10198)
    * "GPT-2": **Radford et. al.** [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) [github.com/openai/gpt-2](https://github.com/openai/gpt-2) [OpenAI: Better Language Models](https://openai.com/research/better-language-models) [Fermats Library](https://www.fermatslibrary.com/s/language-models-are-unsupervised-multitask-learners)

diff --git a/LLM.md b/LLM.md
@@ -71,7 +71,7 @@ Neural network links before starting with transformers.
 * **Ted Chiang** [ChatGPT Is a Blurry JPEG of the Web](https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web)
 * **Noam Chomsky** [The False Promise of ChatGPT](https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html)
 * **Janus** [Simulators](https://generative.ink/posts/simulators/) This is a long post but the main point you can take from it is that LLMs act as simulators that can create many different personas to generate text. Related, easier to read and understand [Janus' Simulators](https://astralcodexten.substack.com/p/janus-simulators)
-* **Julian Togelius** [http://togelius.blogspot.com/2023/04/is-elden-ring-existential-risk-to.html](http://togelius.blogspot.com/2023/04/is-elden-ring-existential-risk-to.html) Satire. This leads into a critique of the concept of intelligence.
+* **Julian Togelius** [Is Elden Ring an existential risk to humanity?](http://togelius.blogspot.com/2023/04/is-elden-ring-existential-risk-to.html) Satire. This leads into a critique of the concept of intelligence.
 
 # Usage
 

diff --git a/LLM.md b/LLM.md
@@ -53,9 +53,8 @@ Neural network links before starting with transformers.
 
 # Research Paper Lists
 
-* **Sebastian Raschka** [Understanding Large Language Models -- A Transformative Reading List](https://sebastianraschka.com/blog/2023/llm-reading-list.html) This article lists some of the most important papers in the area.
+* **Sebastian Raschka** [Understanding Large Language Models -- A Transformative Reading List](https://sebastianraschka.com/blog/2023/llm-reading-list.html) This article lists some of the most important papers in the area. This is a really good chronological list of papers.
 * **OpenAI** [Research Index](https://openai.com/research)
-* **Sebastian Raschka** [Understanding Large Language Models](https://magazine.sebastianraschka.com/p/understanding-large-language-models) This is a really good chronological list of papers.
 
 # Research Papers
 

diff --git a/LLM.md b/LLM.md
@@ -59,7 +59,7 @@ Neural network links before starting with transformers.
 
 # Research Papers
 
-* 1️⃣ **Radford et. al.** [Improving Language Understanding by Generative Pre-Training (2018)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) (GPT-1) a page accompanying this paper on the OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/language-unsupervised)
+* 1️⃣ **Radford et. al.** [Improving Language Understanding by Generative Pre-Training (2018)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) (GPT-1) a page accompanying this paper on the OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/language-unsupervised). Source code (tidied up by thomwolf) here: [huggingface.co/.../openai-gpt](https://huggingface.co/docs/transformers/model_doc/openai-gpt)
 * 2️⃣ **Radford et. al.** [Language Models are Unsupervised Multitask Learners (2019)](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) (GPT-2) accompanying OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/better-language-models). Source code here: [github.com/openai/gpt-2](https://github.com/openai/gpt-2)
 * 3️⃣ **Brown et. al.** [Language Models are Few-Shot Learners](https://openai.com/research/language-models-are-few-shot-learners)
 * **Kaplan et. al.** [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) A variety of models were trained using varying amounts of compute, data set size, and number of parameters. This enables us to predict what parameters will work well in larger future models. See also **Gwern Branwen** [The Scaling Hypothesis](https://gwern.net/scaling-hypothesis)

diff --git a/LLM.md b/LLM.md
@@ -60,7 +60,7 @@ Neural network links before starting with transformers.
 # Research Papers
 
 * 1️⃣ **Radford et. al.** [Improving Language Understanding by Generative Pre-Training (2018)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) (GPT-1) a page accompanying this paper on the OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/language-unsupervised)
-* 2️⃣ **Radford et. al.** [Language Models are Unsupervised Multitask Learners (2019)](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) (GPT-2) accompanying OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/better-language-models)
+* 2️⃣ **Radford et. al.** [Language Models are Unsupervised Multitask Learners (2019)](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) (GPT-2) accompanying OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/better-language-models). Source code here: [github.com/openai/gpt-2](https://github.com/openai/gpt-2)
 * 3️⃣ **Brown et. al.** [Language Models are Few-Shot Learners](https://openai.com/research/language-models-are-few-shot-learners)
 * **Kaplan et. al.** [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) A variety of models were trained using varying amounts of compute, data set size, and number of parameters. This enables us to predict what parameters will work well in larger future models. See also **Gwern Branwen** [The Scaling Hypothesis](https://gwern.net/scaling-hypothesis)
 

diff --git a/LLM.md b/LLM.md
@@ -61,7 +61,7 @@ Neural network links before starting with transformers.
 
 * 1️⃣ **Radford et. al.** [Improving Language Understanding by Generative Pre-Training (2018)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) (GPT-1) a page accompanying this paper on the OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/language-unsupervised)
 * 2️⃣ **Radford et. al.** [Language Models are Unsupervised Multitask Learners (2019)](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) (GPT-2) accompanying OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/better-language-models)
-* 3️⃣4️**Brown et. al.** [Language Models are Few-Shot Learners](https://openai.com/research/language-models-are-few-shot-learners)
+* 3️⃣ **Brown et. al.** [Language Models are Few-Shot Learners](https://openai.com/research/language-models-are-few-shot-learners)
 * **Kaplan et. al.** [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) A variety of models were trained using varying amounts of compute, data set size, and number of parameters. This enables us to predict what parameters will work well in larger future models. See also **Gwern Branwen** [The Scaling Hypothesis](https://gwern.net/scaling-hypothesis)
 
 # Philosophy of GPT

diff --git a/LLM.md b/LLM.md
@@ -59,8 +59,9 @@ Neural network links before starting with transformers.
 
 # Research Papers
 
-* **Radford et. al.** [Improving Language Understanding by Generative Pre-Training (2018)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) (GPT-1) a page accompanying this paper on the OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/language-unsupervised)
-* **Radford et. al.** [Language Models are Unsupervised Multitask Learners (2019)](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) (GPT-2) accompanying OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/better-language-models)
+* 1️⃣ **Radford et. al.** [Improving Language Understanding by Generative Pre-Training (2018)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) (GPT-1) a page accompanying this paper on the OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/language-unsupervised)
+* 2️⃣ **Radford et. al.** [Language Models are Unsupervised Multitask Learners (2019)](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) (GPT-2) accompanying OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/better-language-models)
+* 3️⃣4️**Brown et. al.** [Language Models are Few-Shot Learners](https://openai.com/research/language-models-are-few-shot-learners)
 * **Kaplan et. al.** [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) A variety of models were trained using varying amounts of compute, data set size, and number of parameters. This enables us to predict what parameters will work well in larger future models. See also **Gwern Branwen** [The Scaling Hypothesis](https://gwern.net/scaling-hypothesis)
 
 # Philosophy of GPT

diff --git a/LLM.md b/LLM.md
@@ -59,7 +59,8 @@ Neural network links before starting with transformers.
 
 # Research Papers
 
-* **Radford et. al.** [Improving Language Understanding by Generative Pre-Training (2018)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) a page accompanying this paper on the OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/language-unsupervised)
+* **Radford et. al.** [Improving Language Understanding by Generative Pre-Training (2018)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) (GPT-1) a page accompanying this paper on the OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/language-unsupervised)
+* **Radford et. al.** [Language Models are Unsupervised Multitask Learners (2019)](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) (GPT-2) accompanying OpenAI blog [Improving language understanding with unsupervised learning](https://openai.com/research/better-language-models)
 * **Kaplan et. al.** [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) A variety of models were trained using varying amounts of compute, data set size, and number of parameters. This enables us to predict what parameters will work well in larger future models. See also **Gwern Branwen** [The Scaling Hypothesis](https://gwern.net/scaling-hypothesis)
 
 # Philosophy of GPT

diff --git a/LLM.md b/LLM.md
@@ -46,7 +46,7 @@ Neural network links before starting with transformers.
 * 🟠 **Sasha Rush et. al.** [The Annotated Transformer](http://nlp.seas.harvard.edu/annotated-transformer/)
 * 🟢 **Jay Alammar** [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) companion video above.
   * 🟠 **Jay Alammar** [The Illustrated GPT-2 (Visualizing Transformer Language Models)](https://jalammar.github.io/illustrated-gpt2/)
-  * 🟢 **Jay Alammar** [How GPT3 Works - Visualizations and Animations]([https://jalammar.github.io/illustrated-gpt2/)](https://jalammar.github.io/how-gpt3-works-visualizations-animations/)
+  * 🟢 **Jay Alammar** [How GPT3 Works - Visualizations and Animations](https://jalammar.github.io/how-gpt3-works-visualizations-animations/)
 * 🔥 **Chris Olah et. al.** [In-context Learning and Induction Heads](https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html) companion video lecture above
 * 🟢 **Finbarr Timbers** [Five years of GPT progress](https://finbarr.ca/five-years-of-gpt-progress/)
 * 🟠 **Sebastian Raschka** [Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch](https://sebastianraschka.com/blog/2023/self-attention-from-scratch.html)

diff --git a/LLM.md b/LLM.md
@@ -45,6 +45,8 @@ Neural network links before starting with transformers.
 * 🟠 **PyTorch** [Language Modeling with nn.Transformer and TorchText](https://pytorch.org/tutorials/beginner/transformer_tutorial.html)
 * 🟠 **Sasha Rush et. al.** [The Annotated Transformer](http://nlp.seas.harvard.edu/annotated-transformer/)
 * 🟢 **Jay Alammar** [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) companion video above.
+  * 🟠 **Jay Alammar** [The Illustrated GPT-2 (Visualizing Transformer Language Models)](https://jalammar.github.io/illustrated-gpt2/)
+  * 🟢 **Jay Alammar** [How GPT3 Works - Visualizations and Animations]([https://jalammar.github.io/illustrated-gpt2/)](https://jalammar.github.io/how-gpt3-works-visualizations-animations/)
 * 🔥 **Chris Olah et. al.** [In-context Learning and Induction Heads](https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html) companion video lecture above
 * 🟢 **Finbarr Timbers** [Five years of GPT progress](https://finbarr.ca/five-years-of-gpt-progress/)
 * 🟠 **Sebastian Raschka** [Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch](https://sebastianraschka.com/blog/2023/self-attention-from-scratch.html)

diff --git a/LLM.md b/LLM.md
@@ -48,12 +48,12 @@ Neural network links before starting with transformers.
 * 🔥 **Chris Olah et. al.** [In-context Learning and Induction Heads](https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html) companion video lecture above
 * 🟢 **Finbarr Timbers** [Five years of GPT progress](https://finbarr.ca/five-years-of-gpt-progress/)
 * 🟠 **Sebastian Raschka** [Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch](https://sebastianraschka.com/blog/2023/self-attention-from-scratch.html)
-* **Sebastian Raschka** [Understanding Large Language Models](https://magazine.sebastianraschka.com/p/understanding-large-language-models) Another jumping off point for learning LLMs from, very good.
 
 # Research Paper Lists
 
 * **Sebastian Raschka** [Understanding Large Language Models -- A Transformative Reading List](https://sebastianraschka.com/blog/2023/llm-reading-list.html) This article lists some of the most important papers in the area.
 * **OpenAI** [Research Index](https://openai.com/research)
+* **Sebastian Raschka** [Understanding Large Language Models](https://magazine.sebastianraschka.com/p/understanding-large-language-models) This is a really good chronological list of papers.
 
 # Research Papers
 

diff --git a/LLM.md b/LLM.md
@@ -48,6 +48,7 @@ Neural network links before starting with transformers.
 * 🔥 **Chris Olah et. al.** [In-context Learning and Induction Heads](https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html) companion video lecture above
 * 🟢 **Finbarr Timbers** [Five years of GPT progress](https://finbarr.ca/five-years-of-gpt-progress/)
 * 🟠 **Sebastian Raschka** [Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch](https://sebastianraschka.com/blog/2023/self-attention-from-scratch.html)
+* **Sebastian Raschka** [Understanding Large Language Models](https://magazine.sebastianraschka.com/p/understanding-large-language-models) Another jumping off point for learning LLMs from, very good.
 
 # Research Paper Lists
 

diff --git a/LLM.md b/LLM.md
@@ -87,6 +87,7 @@ Neural network links before starting with transformers.
 * https://github.com/densmirnov/git2gpt/commits/main - create git commits from repo + prompts, mutating a codebase over time
 * https://www.chatpdf.com/ - Upload a PDF and discuss it.
 * https://ggpt.43z.one/ - prompt injection golfing game
+* https://www.debate-devil.com/en - devils advocate debate game
 
 ## ConLang stuff