Garage: Generative-Augmented Retrieval Assisting Generation Enhancement
With the recent rise in popularity of generative models (e.g. ChatGPT, GPT4) the issues with the accuracy of the provided information became a great threat. The models might generate a correct answer, but in many cases, they output with high confidence a totally wrong and extremely plausible answer. In many sectors, this hallucinating behavior can have critical consequences e.g. medicine, law, or engineering, and therefore the usage of generative models is very risky especially when people don’t know the limitations of such tools. In this paper, we tackle a very important task of augmenting generative models like BART or ChatGPT to improve their capabilities of generating factual responses. Moreover, we incorporate a mechanism of providing passages containing information from a local knowledge database alongside the generated response. Thanks to such improvements the users get the possibility to quickly assess the correctness of a response and by relying on an external nonparametric knowledge-base memory it is easy to update the model’s knowledge to provide correct answers. Our model consists of a powerful ensemble of classical and neural retrievers and generative prompt enhancement to achieve a superior performance of information retrieval. Our experiments employ the CovidQA dataset, which comprises questions and passages from scientific articles, to assess the performance of GARAGE. The results demonstrate that our approach outperforms the baseline models in terms of retrieval accuracy and answer quality, while also reducing hallucinations typically encountered in large language models. The total cost of training the model and performing experiments was 10$ making it very affordable and compute resource efficient. GARAGE signifies a promising advancement in open-domain question answering systems and paves the way for future research in combining traditional retrieval methods with neural approaches.
Krzysztof Jankowski is a final year Machine Learning MSc student at University of Warsaw, member of TensorCell research group and an ex-Amazonian. He also worked in 2 startups and learned a lot about entrepreneurship by winning a startup accelerator program organized by UW Incubator. Krzysztof is particularly passionate about Natural Language Processing and Reinforcement Learning and has started contributing to these fields. He loves connecting academic research with business challenges and applying Machine Learning solutions to such problems. In his free time, he enjoys attending ML events and meeting amazing people.