Previously: Paper Readings on LLM Task Performing
I’m still combing through the papers on LLM and I’ll summarize my readings here on this blog. There’s an increasing amount of interest and attention in this field and hence there will be many more papers in the forseeable future. Hopefully I can squeeze more time to read and share my experience with all of you.
Recently I’ve looked into the LangChain project and I was surprised by how it could be such a powerful and mature a project built in such short span of time. It covers many essential tools for creating your own LLM-driven projects, abstracting cumbersome steps with only a few lines of code.
I like where the project direction is going, and the development team has been proactively including and introducing new ideas of the latest LLM features in the project.
The path to understanding this new project weren’t really smooth. It has its own opinions for code organization and it could be unintuitive to guess how to hack your own projects for more than the tutorials. Many of the tutorials out there explains how to create a small application with LangChain but doesn’t cover how to intuitively comprehend the abstraction and design choices.
Hence I have taken the initiative to document my personal cognitive process throughout this journey. By doing so, I aim to clarify my own understanding while also providing assistance to y’all who are interested in hacking LangChain for fun and profit.
This blog post will dedicate to the overall understanding of all the concepts. I found it really helpful to start by understanding the concepts that directly interacts with the LLM, especially the core API interfaces. Once you have the mindmap of all the LangChain abstractions, it’s much more intuitive to hack and extend your own implementation.
I’ve spent the last couple of months reading about the development of AI and NLP development in general ever since the release of ChatGPT. And here’s some of my personal findings specifically on task performing capabilities.
The field of AI has been advancing rapidly, and the results have exceeded expectations for many users and researchers. One particularly impressive development is the Language Model (LLM), which has demonstrated a remarkable ability to generate natural, human-like text. Another exciting example is ChatGPT from OpenAI, which has shown impressive task performing and logical reasoning capabilities.
Looking ahead, I am optimistic that LLM will continue to be incredibly effective at performing more complex tasks with the help of plugins, prompt engineering, and some human input/interactions. The potential applications for LLM are vast and promising.
I’ve compiled a list of papers of extending the task performing capabilities in this field. I’m quite enthusiastic and excited about the potential of longer term of this capability that brings LLMs like ChatGPT to more powerful applications.
Here’s my first list of paper, also what I consider to be more fundamental papers, along with my very quick summaries.