@ -82,6 +82,7 @@ People are writing great tools and papers for improving outputs from GPT. Here a
- [Scale Spellbook](https://scale.com/spellbook): A paid product for building, comparing, and shipping language model apps.
- [Scale Spellbook](https://scale.com/spellbook): A paid product for building, comparing, and shipping language model apps.
- [PromptPerfect](https://promptperfect.jina.ai/prompts): A paid product for testing and improving prompts.
- [PromptPerfect](https://promptperfect.jina.ai/prompts): A paid product for testing and improving prompts.
- [Weights & Biases](https://wandb.ai/site/solutions/llmops): A paid product for tracking model training and prompt engineering experiments.
- [Weights & Biases](https://wandb.ai/site/solutions/llmops): A paid product for tracking model training and prompt engineering experiments.
- [OpenAI Evals](https://github.com/openai/evals): An open-source library for evaluating task performance of language models and prompts.
### Prompting guides
### Prompting guides
@ -97,7 +98,6 @@ People are writing great tools and papers for improving outputs from GPT. Here a
- [Andrej Karpathy's Let's build GPT](https://www.youtube.com/watch?v=kCc8FmEb1nY): A detailed dive into the machine learning underlying GPT.
- [Andrej Karpathy's Let's build GPT](https://www.youtube.com/watch?v=kCc8FmEb1nY): A detailed dive into the machine learning underlying GPT.
- [Prompt Engineering by DAIR.AI](https://www.youtube.com/watch?v=dOxUroR57xs): A one-hour video on various prompt engineering techniques.
- [Prompt Engineering by DAIR.AI](https://www.youtube.com/watch?v=dOxUroR57xs): A one-hour video on various prompt engineering techniques.
### Papers on advanced prompting to improve reasoning
### Papers on advanced prompting to improve reasoning
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022)](https://arxiv.org/abs/2201.11903): Using few-shot prompts to ask models to think step by step improves their reasoning. PaLM's score on math word problems (GSM8K) go from 18% to 57%.
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (2022)](https://arxiv.org/abs/2201.11903): Using few-shot prompts to ask models to think step by step improves their reasoning. PaLM's score on math word problems (GSM8K) go from 18% to 57%.
@ -111,7 +111,6 @@ People are writing great tools and papers for improving outputs from GPT. Here a
- [Reflexion: an autonomous agent with dynamic memory and self-reflection (2023)](https://arxiv.org/abs/2303.11366): Retrying tasks with memory of prior failures improves subsequent performance.
- [Reflexion: an autonomous agent with dynamic memory and self-reflection (2023)](https://arxiv.org/abs/2303.11366): Retrying tasks with memory of prior failures improves subsequent performance.
- [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP (2023)](https://arxiv.org/abs/2212.14024): Models augmented with knowledge via a "retrieve-then-read" can be improved with multi-hop chains of searches.
- [Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP (2023)](https://arxiv.org/abs/2212.14024): Models augmented with knowledge via a "retrieve-then-read" can be improved with multi-hop chains of searches.
## Contributing
## Contributing
If there are examples or guides you'd like to see, feel free to suggest them on the [issues page]. We are also happy to accept high quality pull requests, as long as they fit the scope of the repo.
If there are examples or guides you'd like to see, feel free to suggest them on the [issues page]. We are also happy to accept high quality pull requests, as long as they fit the scope of the repo.
Ted Sanders
1 year ago