Repo Title: LLM Paper Analyzer
Description:
This repository contains an application that leverages an advanced AI research assistant to automatically analyze academic papers related to deep learning and large language models (LLMs). The app provides comprehensive insights into the cited literature, supporting a deeper understanding of the research context and factual basis of each paper.
Features:
- Bulk Paper Analysis: Analyze multiple papers simultaneously, saving time and effort.
- Citation Extraction and Mapping: Automatically extracts all citations from a paper and creates a map of the cited literature, highlighting key references and their relationships.
- Claim-Citation Linking: Identifies important claims or facts within the paper and links them to the specific citations used as support.
- Contextualized Citation Analysis: Provides explanations for the relevance and importance of each key citation within the paper's argument.
- Key Insight Identification: Extracts the most important insights and findings from the paper and lists the primary citations used to support them.
- Experimental Methodology Analysis: Describes the experimental setup and identifies cited works that influenced the methodology.
- Results Comparison: Summarizes the main results and compares them with existing literature using cited works.
- Discussion and Related Work Analysis: Analyzes how the authors situate their work within the existing research landscape, highlighting key citations used in the discussion.
- Future Work and Open Questions Identification: Identifies areas for further research suggested by the authors and notes any supporting citations.
- Critical Analysis of Citation Usage: Evaluates the effectiveness of citation usage and identifies potential biases or areas for improvement.
- Summarized Reports: Generates concise and informative reports summarizing the paper's contribution, key insights, and influential citations.
Benefits:
- Accelerated Literature Review: Quickly grasp the core arguments and supporting evidence of multiple papers.
- Enhanced Research Understanding: Gain a deeper understanding of the research context and the evolution of ideas within a field.
- Improved Research Efficiency: Save time and effort by automating the tedious process of analyzing citations and related work.
- Facilitate Deeper Analysis: Provides a strong foundation for further in-depth analysis and critical evaluation of research papers.
Target Audience:
- Researchers and students in deep learning and LLMs.
- Anyone interested in understanding the factual basis and research context of academic papers.
Technology Stack:
- Python
- [Gemini API]
License:
This project is licensed under the [Apache 2.0 License] license.