University of Kwazulu-Natal

Meet Factool A Task And Domain Agnostic Framework For Reddit

Bonisiwe Shabane
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meet factool a task and domain agnostic framework for reddit

Factuality Leaderboard | Installation | Quick Start | ChatGPT Plugin with FacTool | Citation | This repository contains the source code and plugin configuration for our paper. This repository also contains the resources for Halu-J, which introduces an open-source model for critique-based hallucination judge. Factool is a tool augmented framework for detecting factual errors of texts generated by large language models (e.g., ChatGPT). Factool now supports 4 tasks: Our factuality leaderboard shows the factual accuracy of different chatbots evaluated by FacTool.

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Please reload this page. Large language models (LLMs) have achieved remarkable capabilities in text generation, but their widespread adoption faces a critical challenge: the generation of factually incorrect information, commonly referred to as "hallucinations." FACTOOL addresses this fundamental... Unlike previous approaches that focus on specific tasks like summarization or question answering, FACTOOL leverages external tools and LLM reasoning to provide comprehensive factuality verification across diverse domains. Figure 1: The FACTOOL framework operates in five sequential steps: (1) Claim Extraction from LLM responses, (2) Query Generation for each claim, (3) Tool Querying to collect evidence, (4) Evidence Collection from external sources,... FACTOOL employs a five-stage pipeline that systematically transforms unstructured LLM outputs into verifiable factual assessments. The framework's strength lies in its modular design, allowing adaptation across different tasks while maintaining consistent verification principles.

The first stage addresses the challenge of identifying verifiable units within lengthy, unstructured text. Rather than relying on fixed linguistic boundaries like sentences, FACTOOL uses LLMs themselves to extract claims based on task-specific definitions: This approach leverages LLMs' instruction-following capabilities to handle the varying granularities and structures found across different domains.

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Factuality Leaderboard | Installation | Quick Start | ChatGPT Plugin

Factuality Leaderboard | Installation | Quick Start | ChatGPT Plugin with FacTool | Citation | This repository contains the source code and plugin configuration for our paper. This repository also contains the resources for Halu-J, which introduces an open-source model for critique-based hallucination judge. Factool is a tool augmented framework for detecting factual errors of texts generated by l...

ArXivLabs Is A Framework That Allows Collaborators To Develop And

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add v...

Please Reload This Page. Large Language Models (LLMs) Have Achieved

Please reload this page. Large language models (LLMs) have achieved remarkable capabilities in text generation, but their widespread adoption faces a critical challenge: the generation of factually incorrect information, commonly referred to as "hallucinations." FACTOOL addresses this fundamental... Unlike previous approaches that focus on specific tasks like summarization or question answering, F...

The First Stage Addresses The Challenge Of Identifying Verifiable Units

The first stage addresses the challenge of identifying verifiable units within lengthy, unstructured text. Rather than relying on fixed linguistic boundaries like sentences, FACTOOL uses LLMs themselves to extract claims based on task-specific definitions: This approach leverages LLMs' instruction-following capabilities to handle the varying granularities and structures found across different doma...