LLM Theory
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Awesome LLM theory articles I chanced upon
Blog posts
2023
Simple tricks to improve Retrieval Augmented Generation (RAG) systems
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Motivation
Retrieval Augmented Generation (RAG) is a framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information. RAG is increasingly popular in industry as it’s simple to implement yet powerful. Here I’ll share some tricks to improve RAG systems.
Given the same compute budget, which is better: parameter efficient finetuning (PEFT) or full finetuning?
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Motivation
If we are allowed to train till convergence, we know that full finetuning is better than parameter efficient finetuning (PEFT). But what if we have a fixed compute budget? Given a fixed budget, PEFT can go through significantly more tokens. Will full finetuning still be better than PEFT?
Simple solution to training causal LMs with seq2seq objective
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Motivation
How do we train causal language models (e.g. Alpaca, LLaMA, gpt-neox-20b…) with seq2seq objective? This goal is important because we want to instruction-tune our causal LMs, especially since Alpaca is the best open model at time of writing.
2022
Engineering Tricks
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Compilation of tricks I find useful.
Undergraduate Study Tips
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Motivation
With yesterday’s results release, I have finally graduated! Having been tested for many years of my life, here are some study tips I found useful for my own undergraduate studies. I hope they come in useful for a junior out there :).
Random Machine Learning Theory
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Just my personal notes!
2021
Paper Reviews
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Motivation
As I step into this world of research, I realised I’m still in the midst of discovering what I’m truly interested in. To this end, I hope to read papers solely because I’m interested in it (instead of, say, to do literature review for my current research).
Crash Course on using SoC Compute Clusters
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Need access to NUS computing resources but not sure how? Here’s a quick crash course!