Scientific QA System with Verifiable Answers

TL;DR

This paper presents VerifAI, an open-source scientific QA system that combines retrieval, generation, and verification modules to produce and validate factual scientific answers, reducing hallucinations and increasing trust.

Contribution

The paper introduces VerifAI, a novel system integrating retrieval, generative, and verification components specifically designed for scientific question answering.

Findings

Effective retrieval of scientific papers from PubMed.

Generation of claims with references using fine-tuned models.

Verification engine reduces hallucinations in generated answers.

Abstract

In this paper, we introduce the VerifAI project, a pioneering open-source scientific question-answering system, designed to provide answers that are not only referenced but also automatically vetted and verifiable. The components of the system are (1) an Information Retrieval system combining semantic and lexical search techniques over scientific papers (PubMed), (2) a Retrieval-Augmented Generation (RAG) module using fine-tuned generative model (Mistral 7B) and retrieved articles to generate claims with references to the articles from which it was derived, and (3) a Verification engine, based on a fine-tuned DeBERTa and XLM-RoBERTa models on Natural Language Inference task using SciFACT dataset. The verification engine cross-checks the generated claim and the article from which the claim was derived, verifying whether there may have been any hallucinations in generating the claim. By leveraging the Information Retrieval and RAG modules, Verif.ai excels in generating factual information from a vast array of scientific sources. At the same time, the Verification engine rigorously double-checks this output, ensuring its accuracy and reliability. This dual-stage process plays a crucial role in acquiring and confirming factual information, significantly enhancing the information landscape. Our methodology could significantly enhance scientists' productivity, concurrently fostering trust in applying generative language models within scientific domains, where hallucinations and misinformation are unacceptable.