# KGI: An Integrated Framework for Knowledge Intensive Language Tasks

arXiv: https://arxiv.org/abs/2204.03985 · HTML: https://mlsanity.com/paper/2204.03985/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Conversational and speech RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Placed in

- Retrieval-augmented generation / [Conversational and speech RAG](https://mlsanity.com/rag/conversational-and-speech-rag/)

## Builds on (most cited first, 10 of 10)

- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [KILT: a Benchmark for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2009.02252/) (2020)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus](https://mlsanity.com/paper/2112.09924/) (2021)
- [Multi-task Retrieval for Knowledge-Intensive Tasks](https://mlsanity.com/paper/2101.00117/) (2021)
- [Hindsight: Posterior-guided training of retrievers for improved open-ended generation](https://mlsanity.com/paper/2110.07752/) (2021)
- [Robust Retrieval Augmented Generation for Zero-shot Slot Filling](https://mlsanity.com/paper/2108.13934/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Survey on Knowledge-Enhanced Pre-trained Language Models](https://mlsanity.com/paper/2212.13428/) (2022)
- [Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Case](https://mlsanity.com/paper/2207.05188/) (2022)
- [Science Checker Reloaded: A Bidirectional Paradigm for Transparency and Logical Reasoning](https://mlsanity.com/paper/2402.13897/) (2024)

## Abstract (arXiv)

In this paper, we present a system to showcase the capabilities of the latest state-of-the-art retrieval augmented generation models trained on knowledge-intensive language tasks, such as slot filling, open domain question answering, dialogue, and fact-checking. Moreover, given a user query, we show how the output from these different models can be combined to cross-examine the outputs of each other. Particularly, we show how accuracy in dialogue can be improved using the question answering model. We are also releasing all models used in the demo as a contribution of this paper. A short video demonstrating the system is available at https://ibm.box.com/v/emnlp2022-demo.