mlsanityData to 7 Oct 2026

LLAssist: Simple Tools for Automating Literature Review Using Large Language Models

arXiv 2407.13993 ↗First posted 19 Jul 2024cs.DL cs.AIcited by 4

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Abstract (arXiv)

This paper introduces LLAssist, an open-source tool designed to streamline literature reviews in academic research. In an era of exponential growth in scientific publications, researchers face mounting challenges in efficiently processing vast volumes of literature. LLAssist addresses this issue by leveraging Large Language Models (LLMs) and Natural Language Processing (NLP) techniques to automate key aspects of the review process. Specifically, it extracts important information from research articles and evaluates their relevance to user-defined research questions. The goal of LLAssist is to significantly reduce the time and effort required for comprehensive literature reviews, allowing researchers to focus more on analyzing and synthesizing information rather than on initial screening tasks. By automating parts of the literature review workflow, LLAssist aims to help researchers manage the growing volume of academic publications more efficiently.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 26 references that are in the map. First posted on arXiv 19 Jul 2024.

Builds on

8 of 26 references.

References · 8

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  3. 2020Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performancecited by 396
  4. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  5. 2024LitLLM: A Toolkit for Scientific Literature Reviewcited by 61
  6. 2014A Comparative Study of Programming Languages in Rosetta Codecited by 19
  7. 2023PRISMA-DFLLM: An Extension of PRISMA for Systematic Literature Reviews using Domain-specific Finetuned Large Language Modelscited by 8
  8. 2024Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviewscited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.