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From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility

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arxiv 2402.16142 v1 pith:VRXVI5QV submitted 2024-02-25 cs.CL cs.AI

Pravneet Kaur , Gautam Siddharth Kashyap , Ankit Kumar , Md Tabrez Nafis , Sandeep Kumar , Vikrant Shokeen This is my paper · ORCID
classification cs.CLcs.AI
keywords llmsdomainslanguageclimatecomprehensivedisasterexpansefitness
verification ladder T0 review T1 audit T2 compute T3 formal
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This groundbreaking study explores the expanse of Large Language Models (LLMs), such as Generative Pre-Trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) across varied domains ranging from technology, finance, healthcare to education. Despite their established prowess in Natural Language Processing (NLP), these LLMs have not been systematically examined for their impact on domains such as fitness, and holistic well-being, urban planning, climate modelling as well as disaster management. This review paper, in addition to furnishing a comprehensive analysis of the vast expanse and extent of LLMs' utility in diverse domains, recognizes the research gaps and realms where the potential of LLMs is yet to be harnessed. This study uncovers innovative ways in which LLMs can leave a mark in the fields like fitness and wellbeing, urban planning, climate modelling and disaster response which could inspire future researches and applications in the said avenues.

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Cited by 4 Pith papers

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  2. Automated Testing of the GUI of a Real-Life Engineering Software using Large Language Models

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    GERALLT, a two-LLM-agent system, found five confirmed UI issues in DLR's RCE tool integration wizard during exploratory GUI testing.

  3. A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations

    cs.CR 2025-02 conditional novelty 4.0 of 10

    A survey of LVLM safety that adds a lifecycle taxonomy and new benchmark results showing Janus-Pro-7B has weaker safety than several open-source LVLMs.

  4. Reinforcement Learning for Autonomous Warehouse Orchestration in SAP Logistics Execution: Redefining Supply Chain Agility

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    A Deep Q-Network is reported to achieve 95% task optimization accuracy and 60% faster processing on 300,000 synthetic SAP warehouse transactions.

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