# VerifAI: Verified Generative AI

arXiv: https://arxiv.org/abs/2307.02796 · HTML: https://mlsanity.com/paper/2307.02796/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 64 references that are in the map.
- First posted on arXiv 6 Jul 2023.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [Detecting and Understanding Harmful Memes: A Survey](https://mlsanity.com/paper/2205.04274/) (2022)
- [DeepER -- Deep Entity Resolution](https://mlsanity.com/paper/1710.00597/) (2017)
- [PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training](https://mlsanity.com/paper/2211.02816/) (2022)
- [HoloDetect: Few-Shot Learning for Error Detection](https://mlsanity.com/paper/1904.02285/) (2019)
- [A Benchmark Dataset of Check-worthy Factual Claims](https://mlsanity.com/paper/2004.14425/) (2020)
- [RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation](https://mlsanity.com/paper/2012.02469/) (2020)
- [Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources](https://mlsanity.com/paper/1502.03519/) (2015)
- [Subjective Databases](https://mlsanity.com/paper/1902.09661/) (2019)
- [DIALITE: Discover, Align and Integrate Open Data Tables](https://mlsanity.com/paper/2304.08285/) (2023)

## Built on by (most cited first, 16 of 16)

- [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024)
- [Multi-agent Architecture Search via Agentic Supernet](https://mlsanity.com/paper/2502.04180/) (2025)
- [CRAG -- Comprehensive RAG Benchmark](https://mlsanity.com/paper/2406.04744/) (2024)
- [A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?](https://mlsanity.com/paper/2408.05109/) (2024)
- [KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes](https://mlsanity.com/paper/2506.06541/) (2025)
- [Are Large Language Models Good Statisticians?](https://mlsanity.com/paper/2406.07815/) (2024)
- [Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarking](https://mlsanity.com/paper/2505.11065/) (2025)
- [RobustFlow: Towards Robust Agentic Workflow Generation](https://mlsanity.com/paper/2509.21834/) (2025)
- [DebFlow: Automating Agent Creation via Agent Debate](https://mlsanity.com/paper/2503.23781/) (2025)
- [MASPOB: Bandit-Based Prompt Optimization for Multi-Agent Systems with Graph Neural Networks](https://mlsanity.com/paper/2603.02630/) (2026)
- [Graph Neural Networks for Databases: A Survey](https://mlsanity.com/paper/2502.12908/) (2025)
- [DataPuzzle: Breaking Free from the Hallucinated Promise of LLMs in Data Analysis](https://mlsanity.com/paper/2504.10036/) (2025)
- [ZeroED: Hybrid Zero-shot Error Detection through Large Language Model Reasoning](https://mlsanity.com/paper/2504.05345/) (2025)
- [SPOGW: a Score-based Preference Optimization method via Group-Wise comparison for workflows](https://mlsanity.com/paper/2510.04089/) (2025)
- [SCULPT: Constraint-Guided Pruned MCTS that Carves Efficient Paths for Mathematical Reasoning](https://mlsanity.com/paper/2601.12842/) (2026)
- [Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forward](https://mlsanity.com/paper/2601.13122/) (2026)

## Abstract (arXiv)

Generative AI has made significant strides, yet concerns about the accuracy and reliability of its outputs continue to grow. Such inaccuracies can have serious consequences such as inaccurate decision-making, the spread of false information, privacy violations, legal liabilities, and more. Although efforts to address these risks are underway, including explainable AI and responsible AI practices such as transparency, privacy protection, bias mitigation, and social and environmental responsibility, misinformation caused by generative AI will remain a significant challenge. We propose that verifying the outputs of generative AI from a data management perspective is an emerging issue for generative AI. This involves analyzing the underlying data from multi-modal data lakes, including text files, tables, and knowledge graphs, and assessing its quality and consistency. By doing so, we can establish a stronger foundation for evaluating the outputs of generative AI models. Such an approach can ensure the correctness of generative AI, promote transparency, and enable decision-making with greater confidence. Our vision is to promote the development of verifiable generative AI and contribute to a more trustworthy and responsible use of AI.