Summary of Synthetic Continued Pretraining, by Zitong Yang et al.
Synthetic continued pretraining
by Zitong Yang, Neil Band, Shuangping Li, Emmanuel Candès, Tatsunori Hashimoto
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Pretraining on large-scale internet text enables language models to acquire world knowledge. However, this process is data-inefficient, requiring hundreds or thousands of diverse representations of each fact. This poses a challenge when adapting a model to a small corpus of domain-specific documents, where each fact may appear rarely or only once. To bridge this gap, the authors propose synthetic continued pretraining using EntiGraph, an algorithm that extracts salient entities from source documents and generates diverse text by drawing connections between them. The authors demonstrate that this approach enables language models to answer questions and follow generic instructions related to the source documents without access to them. Additionally, when the source documents are available at inference time, the knowledge acquired through their approach compounds with retrieval-augmented generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super smart computer program that can learn from lots of information on the internet. This program can pick up all sorts of facts and trivia. But, it’s very hard for this program to learn new things when it only has a little bit of information about a specific topic. To solve this problem, scientists developed a way to teach the program using fake data that looks like real data. They call this method “synthetic continued pretraining”. With this approach, the program can answer questions and follow instructions related to a specific topic without having all the information at once. It’s like teaching a student new things by giving them clues and hints instead of just showing them everything. |
Keywords
» Artificial intelligence » Inference » Pretraining » Retrieval augmented generation
