# FaaF: Facts as a Function for the evaluation of generated text

arXiv: https://arxiv.org/abs/2403.03888 · HTML: https://mlsanity.com/paper/2403.03888/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 14 of 14)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories](https://mlsanity.com/paper/2212.10511/) (2022)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://mlsanity.com/paper/1906.00300/) (2019)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [Ragas: Automated Evaluation of Retrieval Augmented Generation](https://mlsanity.com/paper/2309.15217/) (2023)
- [RARR: Researching and Revising What Language Models Say, Using Language Models](https://mlsanity.com/paper/2210.08726/) (2022)
- [The Power of Noise: Redefining Retrieval for RAG Systems](https://mlsanity.com/paper/2401.14887/) (2024)
- [Factuality Enhanced Language Models for Open-Ended Text Generation](https://mlsanity.com/paper/2206.04624/) (2022)
- [FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information](https://mlsanity.com/paper/2106.05707/) (2021)
- [Generating Literal and Implied Subquestions to Fact-check Complex Claims](https://mlsanity.com/paper/2205.06938/) (2022)

## Built on by (most cited first, 5 of 5)

- [Evaluating RAG-Fusion with RAGElo: an Automated Elo-based Framework](https://mlsanity.com/paper/2406.14783/) (2024)
- [Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems](https://mlsanity.com/paper/2411.19710/) (2024)
- [Can LLMs Be Trusted for Evaluating RAG Systems? A Survey of Methods and Datasets](https://mlsanity.com/paper/2504.20119/) (2025)
- [A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG Systems](https://mlsanity.com/paper/2406.14972/) (2024)
- [Automatic Fact-checking in English and Telugu](https://mlsanity.com/paper/2509.26415/) (2025)

## Abstract (arXiv)

The demand for accurate and efficient verification of information in texts generated by large language models (LMs) is at an all-time high, but remains unresolved. Recent efforts have focused on extracting and verifying atomic facts from these texts via prompting LM evaluators. However, we demonstrate that this method of prompting is unreliable when faced with incomplete or inaccurate reference information. We introduce Facts as a Function (FaaF), a new approach to the fact verification task that leverages the function-calling capabilities of LMs. FaaF significantly enhances the ability of LMs to identify unsupported facts in texts, while also improving efficiency and significantly lowering costs compared to prompt-based methods. Additionally, we propose a framework for evaluating factual recall in Retrieval Augmented Generation (RAG) systems, which we employ to compare prompt-based and FaaF methods using various LMs under challenging conditions.