LOCA: Logical Chain Augmentation for Scientific Corpus Cleaning

Overview

LOCA (Logical Chain Augmentation) is a novel framework for automatically cleaning scientific corpora, specifically designed to address the high error rates commonly found in scientific question-answering (QA) datasets.

Installation

Prerequisites

• Python 3.12+
• LLM API key
• uv (recommended)

Setup

1. Clone the repository

git clone xxx
cd LOCA

2. Install dependencies

Using uv (recommended):

uv sync

Project Structure

LOCA/
├── src/ # Source code
│ ├── loca/ # LOCA implementation
│ │ ├── solver.py # Main LOCA solver
│ │ ├── results/ # LOCA results
│ │ └── utils/ # Utilities
│ │ ├── augmentation.py # Augmentation agent
│ │ ├── reviewer.py # Review agents
│ │ └── secretary.py # Secretary for summarizing reviews
│ ├── api/ # LLM API interfaces
│ ...
├── configs/ # Configuration files
├── problem_set/ # Test datasets
├── test_results/ # Evaluation results on other methods
└── scripts/ # Analysis scripts

Usage

Running LOCA

uv run main.py --config PATH_TO_YAML --config-name CONFIG_NAME_IN_YAML

Automated External Consistency Check

./scripts/run_analyze_improved_solutions.sh