vitaLITy 2: Reviewing Academic Literature Using Large Language Models

TL;DR

vitaLITy 2 leverages large language models and semantic embeddings to improve academic literature review processes, enabling more accurate, efficient, and user-friendly searches and summarizations of scholarly papers.

Contribution

It introduces a novel retrieval augmented generation architecture and a user-friendly chat interface for semantic literature search and summarization.

Findings

Enhanced literature search accuracy using semantic embeddings

User-friendly chat interface for complex queries

Open-source availability of the vitaLITy 2 system

Abstract

Academic literature reviews have traditionally relied on techniques such as keyword searches and accumulation of relevant back-references, using databases like Google Scholar or IEEEXplore. However, both the precision and accuracy of these search techniques is limited by the presence or absence of specific keywords, making literature review akin to searching for needles in a haystack. We present vitaLITy 2, a solution that uses a Large Language Model or LLM-based approach to identify semantically relevant literature in a textual embedding space. We include a corpus of 66,692 papers from 1970-2023 which are searchable through text embeddings created by three language models. vitaLITy 2 contributes a novel Retrieval Augmented Generation (RAG) architecture and can be interacted with through an LLM with augmented prompts, including summarization of a collection of papers. vitaLITy 2 also provides a chat interface that allow users to perform complex queries without learning any new programming language. This also enables users to take advantage of the knowledge captured in the LLM from its enormous training corpus. Finally, we demonstrate the applicability of vitaLITy 2 through two usage scenarios. vitaLITy 2 is available as open-source software at https://vitality-vis.github.io.