# factgenie: A Framework for Span-based Evaluation of Generated Texts

arXiv: https://arxiv.org/abs/2407.17863 · HTML: https://mlsanity.com/paper/2407.17863/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 1 of 1)

- [Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation](https://mlsanity.com/paper/2401.10186/) (2024)

## Built on by (most cited first, 2 of 2)

- [Pearmut: Human Evaluation of Translation Made Trivial](https://mlsanity.com/paper/2601.02933/) (2026)
- [TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories](https://mlsanity.com/paper/2511.21322/) (2025)

## Abstract (arXiv)

We present factgenie: a framework for annotating and visualizing word spans in textual model outputs. Annotations can capture various span-based phenomena such as semantic inaccuracies or irrelevant text. With factgenie, the annotations can be collected both from human crowdworkers and large language models. Our framework consists of a web interface for data visualization and gathering text annotations, powered by an easily extensible codebase.