Principle:Mbzuai oryx Awesome LLM Post training Paper Corpus Review
| Knowledge Sources | |
|---|---|
| Domains | Curation, Data_Ingestion |
| Last Updated | 2026-02-08 07:30 GMT |
Overview
A data ingestion step that loads a previously collected paper corpus from a JSON file into memory for manual review and selection.
Description
Paper Corpus Review is the initial step of an awesome-list curation workflow. A large JSON dataset (produced by an automated collection pipeline) is loaded into memory so that a human curator can examine paper titles, abstracts, and TL;DR summaries. The goal is to build familiarity with the corpus and identify which papers are relevant, impactful, and appropriate for inclusion in a curated list.
This step bridges automated data collection with human editorial judgment. The automated pipeline may collect thousands of papers, but only a fraction will meet the quality and relevance thresholds for a curated resource.
Usage
Use this principle when:
- A large paper corpus has been collected programmatically
- Human review is needed to filter for quality and relevance
- The corpus is stored in a structured JSON format with rich metadata
Theoretical Basis
Pseudo-code Logic:
# Abstract corpus review pattern (NOT real implementation)
corpus = load_json("collected_papers.json")
for paper_id, metadata in corpus.items():
review(
title=metadata["Title"],
abstract=metadata["Abstract"],
summary=metadata["TL;DR"],
year=metadata["Publication Year"],
venue=metadata["Venue"]
)
# Human decision: include / exclude / categorize
The review process applies both inclusion criteria (relevance, quality, recency) and exclusion criteria (duplicates, tangential topics, low-quality venues).