KGI: An Integrated Framework for Knowledge Intensive Language Tasks
This work addresses the problem of enhancing accuracy in language tasks like dialogue for AI researchers and practitioners, but it appears incremental as it builds on existing models.
The paper tackled knowledge-intensive language tasks by integrating retrieval-augmented generation models, showing that combining outputs from different models, such as using a question answering model, improved dialogue accuracy.
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.