[pixl97]

Eh, part of the problem is people don't currently understand what LLMs are doing...

Have you ever had a dumb/wrong thought in your head? I'm going to go ahead and answer yes for you, you do all the time. In fact you don't (hopefully) verbalize a stream of consciousness to other people around you. In general you think of something then reflect on what it is true/false.

This is not what LLMs do, they pitch back the first 'thought' they have, "correct" or not. This is why things like COT/TOT greatly increase the accuracy of LLM output. The problem? It requires at least an order of magnitude more processing to get an answer, and with GPU time already in high demand and expensive you don't see much of it happen.

Betting on LLMs commonly being wrong is not a safe bet at this point.

[giantrobot]

Even if the error rate of LLMs decreases with additional GPU power there's little rhyme or reason to their confabulations. Even if only 1% of the code is in error there's no guidance or pattern to where those errors might be.

It's like reviewing an overconfident junior developer's code except you can't learn their particular weaknesses. If a developer is bad about memory leaks, you know to check their every PR for memory leaks. An LLM won't necessarily produce the same types of errors given similar prompts or even the same prompt with some period of time between invocations.

[knewter]

Yes but read the tree of thoughts paper

[renyujia]

BTW here is the link to the Tree of Thoughts paper "Large Language Model Guided Tree-of-Thought":

https://arxiv.org/pdf/2305.08291.pdf

https://github.com/jieyilong/tree-of-thought-puzzle-solver

In this paper, we introduce the Tree-of-Thought (ToT) framework, a novel approach aimed at improving the problem-solving capabilities of auto-regressive large language models (LLMs). The ToT technique is inspired by the human mind’s approach for solving complex reasoning tasks through trial and error. In this process, the human mind explores the solution space through a tree-like thought process, allowing for backtracking when necessary. To implement ToT as a software system, we augment an LLM with additional modules including a prompter agent, a checker module, a memory module, and a ToT controller. In order to solve a given problem, these modules engage in a multi-round conversation with the LLM. The memory module records the conversation and state history of the problem solving process, which allows the system to backtrack to the previous steps of the thought-process and explore other directions from there. To verify the effectiveness of the proposed technique, we implemented a ToT-based solver for the Sudoku Puzzle. Experimental results show that the ToT framework can significantly increase the success rate of Sudoku puzzle solving.