I’ve been following Eric’s posts about SudoLang since the first installment back in March. I’ve skimmed through the spec and the value proposition quite compelling. SudoLang seeks to allow programmers all levels to instruct LLMs and can also be transpiled into your programming language of choice. While I’m still early in my understanding of how to use this technique, it’s one I’m following closely and continuing to experiment with.What if we set GPT-4 free in Minecraft? ⛏️
I’m excited to announce Voyager, the first lifelong learning agent that plays Minecraft purely in-context. Voyager continuously improves itself by writing, refining, committing, and retrieving *code* from a skill library.
GPT-4 unlocks… pic.twitter.com/hjTxk6Qb1x
— Jim Fan (@DrJimFan) May 26, 2023 NVIDIA researchers introduce an LLM-based agent with “lifelong learning” capabilities that can navigate, discover, and accomplish goals in Minecraft without human intervention.The Alexandria Index is building embeddings for large, public data sets, to make them more searchable and accessible.
That people produce HTML with string templates is telling us something. I think about this phenomena often, though I personally find most string template systems that produce HTML difficult to use. Django templates, Handlebars, Rails ERB, Hugo templates just to name a few. My experience has been these systems are difficult to debug and are practically their own full programming languages.I’ve seen a lot of “GPT detection” products floating around lately. Sebastian discusses some of the products and their approaches in this article. Some products claim to have developed an “algorithm with an accuracy rate of text detection higher than 98%”. Unfortunately, this same algorithm determined a GPT-4 generated response from the prompt “write a paragraph in the style of Edgar Allan Poe” was 0% AI GPT. In my experience, you don’t need to try very hard to trick “AI-detection” systems.Brex wrote a nice beginner guide on prompt engineering.A low-effort quality-of-life improvement for oncall has been starting a week-long shift on a Friday instead of a Monday. Beginning a weekend with oncall isn’t the best, but it’s more than offset by how good it feels to finish the week and oncall at the same time next Friday.LMQL is a SQL-like programming language for interacting with LMs. It takes a declarative approach to specifying the output constraints for a language model, with a SQL flavor.
Microsoft created a project called guidance which is an LLM-agnostic language to “interleave generation, prompting, and logical control into a single continuous flow matching how the language model actually processes the text”. It’s based on Handlebars templates and provides in-template notion for system and user messages.marvin’s @ai_model decorator implements something similar to what I had in mind for extracting structured data from an input to a language model.
They also use a phase that I like and may adopt for this approach to formatting the output of a language model:
Format … data declaratively
In most of the examples, structured data is extracted from unstructured input. The docs don’t discuss the use of schema to add additional context to the provided data.Added arbitrary context free grammar constraints to llama.cpp
Can now plug in any llama.cpp compatible model and give an exact grammar spec: JSON, etc
Excited to use with more powerful local models as they are released
Thanks @ggerganov & friends for such a wonderful project. pic.twitter.com/HCLACavrlH
— Grant Slatton (@GrantSlatton) May 14, 2023 Restricting the next predicted token to adhere to a specific context free grammar seems like a big step forward in weaving language models into applications.Using system prompts provides an intuitive separation for input and output schema from input content.
Using system prompts does not effectively guard against prompt injection.