Pith. sign in

REVIEW 1 cited by

Bio-Eng-LMM AI Assist chatbot: A Comprehensive Tool for Research and Education

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2409.07110 v2 pith:CCRUVGQC submitted 2024-09-11 eess.SY cs.SY

Ali Forootani , Danial Esmaeili Aliabadi , Daniela Thraen This is my paper · ORCID
classification eess.SYcs.SY
keywords bio-eng-lmmchatbotgenerationcomprehensiveenhanceimageinformationinteraction
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This article introduces Bio-Eng-LMM AI chatbot, a versatile platform designed to enhance user interaction for educational and research purposes. Leveraging cutting-edge open-source Large Language Models (LLMs), Bio-Eng-LMM operates as a sophisticated AI assistant, exploiting the capabilities of traditional models like ChatGPT. Central to Bio-Eng-LMM is its implementation of Retrieval Augmented Generation (RAG) through three primary methods: integration of preprocessed documents, real-time processing of user-uploaded files, and information retrieval from any specified website. Additionally, the chatbot incorporates image generation via a Stable Diffusion Model (SDM), image understanding and response generation through LLAVA, and search functionality on the internet powered by secure search engine such as DuckDuckGo. To provide comprehensive support, Bio-Eng-LMM offers text summarization, website content summarization, and both text and voice interaction. The chatbot maintains session memory to ensure contextually relevant and coherent responses. This integrated platform builds upon the strengths of RAG-GPT and Web-Based RAG Query (WBRQ) where the system fetches relevant information directly from the web to enhance the LLMs response generation.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Climate Aware Deep Neural Networks (CADNN) for Wind Power Simulation

    cs.LG 2024-12 reject novelty 4.0 of 10

    An LSTM trained on CMIP6 wind speed and pressure data is claimed to outperform MLP and Transformer-LSTM models for simulating wind power in Germany, but the evaluation lacks metrics and uses a leakage-prone random split.

Pith tools