tree-of-thought-llm
tree-of-thought-prompting
tree-of-thought-llm | tree-of-thought-prompting | |
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41 | 8 | |
4,228 | 589 | |
4.3% | - | |
7.2 | 5.3 | |
3 months ago | 5 months ago | |
Python | ||
MIT License | MIT License |
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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
tree-of-thought-prompting
- Ask HN: Any good collection of writing prompts for GPT 3.5/4?
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GitHub - Secrets of Tree of Thoughts for Programmers 🌳👨💻
Tree of Thoughts Prompting or framework is techniques to get the model to diversify its output and self-evaluate its response.
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Questions about memory, tree-of-thought, planning
2 - Probably too early in testing and development for there to be a 'standard'. A quick google search will find you some stuff to read like https://github.com/dave1010/tree-of-thought-prompting, but your best bet is to read through the stuff other people are doing and try things for yourself. You might end up discovering something new that nobody has thought of yet. Kaio Ken literally just changed the game overnight and figured out how to expand context to 8k for llama-based models with 2 lines of code. Things are evolving fast and the community desperately needs people willing to spend time reading papers on Arxiv, digging through githubs, and testing stuff out.
- What size model is needed for Reasoning?
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Puzzle GPT: Highly Effective and Fun Puzzle-Solving Prompt for GPT-4 (Uses CoT & ToT)
Source: Conversation with Bing, 6/4/2023 (1) Chain-of-Thought Prompting | Prompt Engineering Guide. https://www.promptingguide.ai/techniques/cot. (2) [2305.10601] Tree of Thoughts: Deliberate Problem Solving with Large .... https://arxiv.org/abs/2305.10601. (3) [2201.11903] Chain-of-Thought Prompting Elicits Reasoning in Large .... https://arxiv.org/abs/2201.11903. (4) Using Tree-of-Thought Prompting to boost ChatGPT's reasoning. https://github.com/dave1010/tree-of-thought-prompting. (5) Tree of Thoughts: Deliberate Problem Solving with Large Language Models. https://arxiv.org/pdf/2305.10601.pdf. ```
- Tekoäly on jo osittain ohittanut ihmisen. Kehitysvauhdin kiihtyessä tärkeä kysymys on, kenen etiikkaa AI noudattaa. Ykkösaamun vieraana on professori Teemu Roos Suomen tekoälykeskuksesta. Seija Vaaherkumpu haastattelee.
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How close are we to an AutoGPT (or similar programme) that can improve its own code recursively?
That’s not exactly correct. Tree of thought prompting can boost reasoning. Check out the GitHub. https://github.com/dave1010/tree-of-thought-prompting
- Using Tree of Thought Prompting to boost ChatGPT's reasoning
What are some alternatives?
Voyager - An Open-Ended Embodied Agent with Large Language Models
gpt_jailbreak_status - This is a repository that aims to provide updates on the status of jailbreaking the OpenAI GPT language model.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
llama-retrieval-plugin - LLaMa retrieval plugin script using OpenAI's retrieval plugin
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%
Neurite - Fractal Graph Desktop for Ai-Agents, Web-Browsing, Note-Taking, and Code.
hamilton - Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.
Mr.-Ranedeer-AI-Tutor - A GPT-4 AI Tutor Prompt for customizable personalized learning experiences.
SillyTavern - LLM Frontend for Power Users.
tree-of-thought-puzzle-solver - The Tree of Thoughts (ToT) framework for solving complex reasoning tasks using LLMs
LocalAI - :robot: The free, Open Source OpenAI alternative. Self-hosted, community-driven and local-first. Drop-in replacement for OpenAI running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. It allows to generate Text, Audio, Video, Images. Also with voice cloning capabilities.
gorilla - Gorilla: An API store for LLMs