tree-of-thought-llm
json-schema-spec
tree-of-thought-llm | json-schema-spec | |
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41 | 32 | |
4,228 | 3,279 | |
4.3% | 4.7% | |
7.2 | 7.9 | |
3 months ago | 5 days ago | |
Python | JavaScript | |
MIT License | GNU General Public License v3.0 or later |
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tree-of-thought-llm
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AI Chat Applications with the Metacognition Approach: Tree of Thoughts (ToT)
[2305.10601] Tree of Thoughts: Deliberate Problem Solving with Large Language Models (arxiv.org)
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Last night /u/ alesneolith posted a very serious writeup claiming to have worked in one of the projects. The writeup is more elaborate than expected and got surprisingly little attention. His account has been since deleted.
Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, “Tree of Thoughts” (ToT), which generalizes over the popular “Chain of Thought” approach to prompting language models, and enables exploration over coherent units of text (“thoughts”) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. Our experiments show that ToT significantly enhances language models’ problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%. Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-llm.
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Ultra Fast Bert
GPU utilization should be down when using this technique. I’m hoping this could allow for more efficient batch inference on GPUs. If you can predict 10 tokens for the price of 1 it should allow you to do tree of thought much more efficiently.
https://github.com/princeton-nlp/tree-of-thought-llm
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Is it best to not pay attention to AI news and/or find ways to delude ourselves into believing better outcomes?
For those familiar with Daniel Kahneman's Thinking Fast and Slow, the current LLMs (such as GPT-4 via ChatGPT) seem to resemble System 1 thinking (near-instantaneous, automatic, intuitive processes like next-word prediction). However, they lack System 2 thinking (slow, effortful, logical, planning, reasoning). What I learned today is that Google's Gemini (an LLM in training now) not only has more modalities (I think all Youtube Video and audio??), more compute, and almost twice the training data, but they're building in AlphaGo-type learning, which resembles tree of thoughts and looks a LOT like the missing puzzle piece of System 2 thinking. Will it be AGI? Maybe, and it's coming this winter.
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Langchain Is Pointless
Tree of thoughts: https://arxiv.org/abs/2305.10601
Good video on "Tree of thoughts" which also reviews / puts it in the context of other methods: https://www.youtube.com/watch?v=ut5kp56wW_4
Completion vs conversational interface is something you can read about in the OpenAI API documentation.
For the remaining things I don't have single specific pointer at hand.
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To all skeptics with a background in AI/CS : what is your realistic timeline for AGI/ASI ?
What do you think about the combination of Tree of Thoughts: Deliberate Problem Solving with Large Language Models LongNet: Scaling Transformers to 1,000,000,000 Tokens Textbooks Are All You Need Attention Is All You Need Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
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Why do language models appear to work left-to-right?
You are right. Tree of Thoughts: Deliberate Problem Solving with Large Language Models proposes to solve this via MCTS-style generation (similar to how AlphaGo worked, and a lot of planning & control problems are executed).
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Munk Debate on Artificial Intelligence
The transformer was developed in 2017 and it powers all modern LLMs. If you're familiar with Daniel Kahneman's work from Thinking Fast and Slow, you could easily summarize LLMs as excellent System 1 thinking: our fast, automatic, unconscious responses (e.g. autocomplete). I'd argue that we're one development (similar to the transformer) away from creating System 2 thinking: deliberate and strategic thinking. In fact, with merely GPT-4 and some clever architectures, researchers have developed chain-of-thought prompting and, more recently, tree-of-thoughts reasoning. While external to the LLM architecture, embedding these concepts into a LLM could very likely solve the creation of System 2 thinking and produce the first real AGI. Adding more modalities (e.g. audio, images, video, topography, etc.) will simply add more nuance in the weights and biases of a complete system.
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Question regarding model compatibility for Alpaca Turbo
There are a bunch of other methods to improve quality and performance like tree-of-thought-llm, connecting a LLM to a database or have it review its own output.
- Tree of thoughts build in open-source model
json-schema-spec
- Python JSON schema
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TypeSpec: A New Language for API-Centric Development
Yep and that comes from JSON Schema: https://json-schema.org/
I believe recent versions of OpenAPI are "compatible" with JSON Schema (at least they "wanted to be" last I checked as I was implementing some schema converters).
Even TypeScript is not enough to represent all of JSON Schema! But it gets close (perhaps if you remove validation rules and stuff like that it's a full match).
But even something like Java can represent most of it pretty well, specially since sealed interfaces were added. I know because I've done it :).
- JSON Schema Blog
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Deploy a simple data storage API with very little code using Amazon API Gateway and DynamoDB
models.tf where I centralized all the Data model that API Gateway uses to perform input and output checks. Those use the JSON-schema specification. GitHub - psantus/serverless.api-gateway-dynamodb-integration.terraform
- Unlocking the frontend – a call for standardizing component APIs pt.2
- JSON Schema
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How to Automatically Consume RESTful APIs in Your Frontend
In the meantime, we are going to expand our backend with two endpoints: one for fetching data and another one for creating data. Fastify provides out-of-the-box support for API serialization and validation through its schema-based approach built on top of JSON Schema. Through the schema option, we can attach a schema definition to each route.
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A View on Functional Software Architecture
JSON-schema to define templates for request and response contents.
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Learn serverless on AWS step-by-step: Strong Types!
The syntax used to define the output is called JSON Schema. It is a standard way to define the structure of a JSON object. If you know zod, the spirit is similar. Based on Swarmion's roadmap, it will be possible to use zod schemas to defined contracts in the future, which will be super cool!
- XML is better than YAML
What are some alternatives?
Voyager - An Open-Ended Embodied Agent with Large Language Models
outlines - Structured Text Generation
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
guidance - A guidance language for controlling large language models.
tree-of-thoughts - Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%
uplaybook - A python-centric IT automation system.
Neurite - Fractal Graph Desktop for Ai-Agents, Web-Browsing, Note-Taking, and Code.
nix-configs - My Nix{OS} configuration files
hamilton - Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.
OpenAPI-Specification - The OpenAPI Specification Repository
Mr.-Ranedeer-AI-Tutor - A GPT-4 AI Tutor Prompt for customizable personalized learning experiences.
torch-grammar