tree-of-thought-prompting VS txtinstruct

Tree of Thoughts Prompting or framework is techniques to get the model to diversify its output and self-evaluate its response.

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.

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. ```

That’s not exactly correct. Tree of thought prompting can boost reasoning. Check out the GitHub. https://github.com/dave1010/tree-of-thought-prompting

I tried cromadb but had terrible performance and could not pin down the cause (likely a problem on my end). Weaviate was easy to setup and had excellent performance, this is probably what I will use in the future. Next on my list is txtinstruct, to finetune a model with data that does not change and using a vector db for everything else seems promising.

The closest thing I've seen to this is txtinstruct

txtinstruct is a framework for training instruction-tuned models

Great to see the continued release of open models. The only disappointing thing is that models keep building on CC-BY-NC licensed datasets, which severely limits their use.

Hopefully, people consider txtinstruct (https://github.com/neuml/txtinstruct) and other approaches to generate instruction-tuning datasets without the baggage.