Large Languagle Models (LLMs) & Natural Language Processing (NLP)

Est. read time: 1 minute | Last updated: March 31, 2025 by John Gentile

Contents

Overview
Large Language Models (LLM)
References

Overview

Large Language Models (LLM)

Prompt Engineering

Suggestions:

Start with a short and simple prompt, and iterate to get better results.
Put instructions at beginning or end of prompt, while clearly separating instructions from the text of interest.
Describe how the model should behave and respond- for example, if looking for coding advice, can create a system prompt of You are a helpful assistant that answers programming questions.
- Add specificity and descriptions about the task and desired output, as well as including examples of output if possible.
- Instructions should tell what “to do” rather than “what not to do”.

References:

LLMs for Coding

(3/27/2025) so far Claude 3.7 Sonnet has slightly better coding results.

LLMs for Research

Claude, OpenAI/ChatGPT and Grok have “research” modes where the model is open to searching the internet, and has multi-step reasoning to come up with deep assessments and recommendations.

Self-Hosted

LLaMA: Open and Efficient Foundation Language Models - arXiv: Meta AI open-source LLM model.
- llama3 implemented from scratch
- llama.cpp: fast, low overhead inference of LLaMA in C/C++.

Other Uses/Automation

LaurieWired/GhidraMCP: ghidraMCP is an Model Context Protocol server for allowing LLMs to autonomously reverse engineer applications. It exposes numerous tools from core Ghidra functionality to MCP clients.

References

Attention Is All You Need- Arxiv: introduces concepts of transformers and attention
Hugging Face: pre-trained NLP models & reference
Training data-efficient image transformers & distillation through attention- Facebook AI
The Illustrated Transform: NLP walk-through