Co-founder at DAIR.AI | PhD | Prev: Meta AI, Galactica LLM, PapersWithCode, Elastic | Creator of the Prompting Guide (5M+ learners)
LLMs as Compilers This work proposes a think-and-execute framework to decompose the reasoning process in language models. This helps to improve algorithmic reasoning in LLMs. - It first THINKS to discover a task-level logic to solve a task and express logic in pseudocode - It then EXECUTES to simulate the execution of the code with language models Apparently, the process of discovering task-level logic behind a task improves performance on algorithmic reasoning tasks. It also outperforms instance-specific reasoning approaches like chain-of-thought and program-of-thoughts. Pseudocode understanding and generation is powerful and this paper shows how it can improve reasoning in LLMs. This is fascinating as pseudocode is one of my favorite approaches to solving really complex problems and build complex programs. Similar to the paper I shared yesterday on visualization-as-thoughts, I think this trend continues where we borrow ideas from how humans solve problems to improve LLMs and how we interact with them. Paper: https://lnkd.in/eTNJtVWv -- You can also track my weekly summary of LLM papers and research developments here: https://lnkd.in/e6ajg945
Thanks for sharing this. I have been looking for an llm based compiler. LLMs can write code, now they can make more complex code gaining exactly what they were missing. I am looking forward to going through this.
Vector Compute @ Superlinked | xYouTube
4moIntegrating this framework with visualization or analogy-making approaches from other studies could yield even more powerful multipart reasoning abilities.