# nBIIG: A Neural BI Insights Generation System for Table Reporting

arXiv: https://arxiv.org/abs/2211.04417 · HTML: https://mlsanity.com/paper/2211.04417/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 8 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://mlsanity.com/paper/2204.06745/) (2022)
- [Logic2Text: High-Fidelity Natural Language Generation from Logical Forms](https://mlsanity.com/paper/2004.14579/) (2020)
- [Evaluating Semantic Accuracy of Data-to-Text Generation with Natural Language Inference](https://mlsanity.com/paper/2011.10819/) (2020)
- [EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference](https://mlsanity.com/paper/1901.03735/) (2019)

## Built on by (most cited first, 1 of 1)

- [Data-driven Discovery with Large Generative Models](https://mlsanity.com/paper/2402.13610/) (2024)

## Abstract (arXiv)

We present nBIIG, a neural Business Intelligence (BI) Insights Generation system. Given a table, our system applies various analyses to create corresponding RDF representations, and then uses a neural model to generate fluent textual insights out of these representations. The generated insights can be used by an analyst, via a human-in-the-loop paradigm, to enhance the task of creating compelling table reports. The underlying generative neural model is trained over large and carefully distilled data, curated from multiple BI domains. Thus, the system can generate faithful and fluent insights over open-domain tables, making it practical and useful.