vitaLITy 2: Reviewing Academic Literature Using Large Language Models
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 s...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2408.13450 |