Intro

If you could understand this statement: Python function is a first-class function, then I believe you should have no problem understanding the Python decorator too. This statement means that the function is also a value, just like any other primitive types (int, str, float, etc), and can be passed as arguments to function or returned as function outputs.

You may heard of the technical term - high-order function, which means that its arguments contain a function or (and) it returns a function. So we know that the Python decorator is a kind of high-order function.

Motivation

A language solves 2 subproblems.

1. Mapping sentence prefixes to fixed-size representation.
2. Using these representations to predict the next token in the context.

The $k\texttt{NN-LM}$ proposed in this hypothesis that representation learning problem may be easier than the prediction problem

kNN-LM

The following graph demonstrates the idea behind the $k\texttt{NN-LM}$ model.

Data Preparation

To use the $k\texttt{NN-LM}$, we need to preprocess the documents in the corpus. The preprocessing procedure can be divided into some steps. Take the following sentence as an example.

What’ is KNN Algorithm

Syntax

The idea

In-Context RALM¹ is the RAG technology for Autoregressive LM. In summary, the RAG technology involves using a retriever during model inference to fetch relevant documents, which are then concatenated with the origin input.

In the In-Context Learning setting, some examples are placed before the user’s input, and then they are fed to LLM. Similarly, the In-Context RALM works in a similar way: it directly concatenates the most relevant retrieved document in front of the model’s input. The advantage is that there’s no need to retrain the LLM. A diagram created with Mermaid is shown below.

Introduction

Recently I was planning to learn the RAG technology so I started to read some related papers. I found a good Roadmap in the ACL 2023 Tutorial’s slide. Today’s topic is the most fundamental one: Retrieval-Augmented Language Model Pre-Training (REALM).

Intro

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Intro

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Intro

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Intro

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