Factool

Visit Tool

factool is an Open Source & Models tool that detects factual errors in generative AI texts. It supports tasks like knowledge-based QA, code generation, mathematical reasoning, and scientific literature review.

Claim this tool

2Views

At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is factool?

FacTool is an open-source framework designed for factuality detection in texts generated by large language models (LLMs) such as ChatGPT. It helps identify and correct factual errors across various domains. The tool supports four key tasks: knowledge-based QA, where it detects errors in factual questions and answers; code generation, by identifying execution errors in generated code; mathematical reasoning, for detecting calculation errors; and scientific literature review, to pinpoint hallucinated scientific references. FacTool also includes resources for Halu-J, an open-source model focused on critique-based hallucination judgment. It offers a factuality leaderboard to compare the factual accuracy of different chatbots and provides detailed output including claim-level and response-level factuality, reasoning, errors, and corrections.

Best used for

Ideal for developers and data scientists who need to rigorously evaluate the factual accuracy of AI-generated content, detect errors in code, and verify mathematical reasoning. Especially valuable for researchers and engineers working on improving the reliability and trustworthiness of large language models across various applications.

Common actions

detect factual errors

evaluate LLM accuracy

validate AI-generated content

benchmark AI models

collaborationlow-code/no-codeopen-sourceworkflowsdeepfakeautomated workflowgithub copilotface swapping"AI Agents"

Capabilities

Key features

Factuality detection
Knowledge-based QA
Code generation error detection
Mathematical reasoning error detection
Scientific literature review
Factuality leaderboard
Detailed error reporting

Target Audience

developerdata scientistresearcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of factual errors can FacTool detect?

FacTool can detect factual errors in knowledge-based QA, execution errors in code generation, calculation errors in mathematical reasoning, and hallucinated scientific literature. It provides a comprehensive approach to evaluating the factual integrity of AI-generated texts across these diverse tasks.

How does FacTool evaluate the factual accuracy of LLMs?

FacTool evaluates LLMs by providing a factuality leaderboard that shows weighted claim-level and response-level accuracy. It breaks down responses into claims, queries external sources for evidence, and then provides detailed reasoning, identified errors, corrections, and a factuality score for each claim and the overall response.

What API keys are required to use FacTool?

To use FacTool, you will need an OpenAI API key for all scenarios. Additionally, a Serper API key is required for knowledge-based QA tasks, and a Scraper API key is needed for scientific literature review tasks. These keys facilitate external data retrieval and model interaction.

Trending

Subcategories trending in Coding & Development

Code Assistants DevOps & Infrastructure No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

Trending

Explore

Browse AI tools by category

Content & Design Productivity & Business Coding & Development AI Agents & Automation Research & Education Wellness & Lifestyle Career Development Marketing & Growth Data & Analytics Customer Support & CX Finance E-commerce