A Complete Survey on LLM-based AI Chatbots

• URL: http://arxiv.org/abs/2406.16937v1
• Date: Mon, 17 Jun 2024 09:39:34 GMT
• Title: A Complete Survey on LLM-based AI Chatbots
• Authors: Sumit Kumar Dam, Choong Seon Hong, Yu Qiao, Chaoning Zhang,
• Abstract summary: The past few decades have witnessed an upsurge in data, forming the foundation for data-hungry, learning-based AI technology. Conversational agents, often referred to as AI chatbots, rely heavily on such data to train large language models (LLMs) and generate new content (knowledge) in response to user prompts. This paper presents a complete survey of the evolution and deployment of LLM-based chatbots in various sectors.
• Score: 46.18523139094807
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The past few decades have witnessed an upsurge in data, forming the foundation for data-hungry, learning-based AI technology. Conversational agents, often referred to as AI chatbots, rely heavily on such data to train large language models (LLMs) and generate new content (knowledge) in response to user prompts. With the advent of OpenAI's ChatGPT, LLM-based chatbots have set new standards in the AI community. This paper presents a complete survey of the evolution and deployment of LLM-based chatbots in various sectors. We first summarize the development of foundational chatbots, followed by the evolution of LLMs, and then provide an overview of LLM-based chatbots currently in use and those in the development phase. Recognizing AI chatbots as tools for generating new knowledge, we explore their diverse applications across various industries. We then discuss the open challenges, considering how the data used to train the LLMs and the misuse of the generated knowledge can cause several issues. Finally, we explore the future outlook to augment their efficiency and reliability in numerous applications. By addressing key milestones and the present-day context of LLM-based chatbots, our survey invites readers to delve deeper into this realm, reflecting on how their next generation will reshape conversational AI.

Related papers

• Distinguishing Chatbot from Human [1.1249583407496218]
  We develop a new dataset consisting of more than 750,000 human-written paragraphs. Based on this dataset, we apply Machine Learning (ML) techniques to determine the origin of text. Our proposed solutions offer high classification accuracy and serve as useful tools for textual analysis.
  arXiv  Detail & Related papers  (2024-08-03T13:18:04Z)
• A Transformer-based Approach for Augmenting Software Engineering Chatbots Datasets [4.311626046942916]
  We present an automated transformer-based approach to augment software engineering datasets. We evaluate the impact of using the augmentation approach on the Rasa NLU's performance using three software engineering datasets.
  arXiv  Detail & Related papers  (2024-07-16T17:48:44Z)
• Cross-Data Knowledge Graph Construction for LLM-enabled Educational Question-Answering System: A Case Study at HCMUT [2.8000537365271367]
  Large language models (LLMs) have emerged as a vibrant research topic. LLMs face challenges in remembering events, incorporating new information, and addressing domain-specific issues or hallucinations. This article proposes a method for automatically constructing a Knowledge Graph from multiple data sources.
  arXiv  Detail & Related papers  (2024-04-14T16:34:31Z)
• ChatGPT Alternative Solutions: Large Language Models Survey [0.0]
  Large Language Models (LLMs) have ignited a surge in research contributions within this domain. Recent years have witnessed a dynamic synergy between academia and industry, propelling the field of LLM research to new heights. This survey furnishes a well-rounded perspective on the current state of generative AI, shedding light on opportunities for further exploration, enhancement, and innovation.
  arXiv  Detail & Related papers  (2024-03-21T15:16:50Z)
• History of generative Artificial Intelligence (AI) chatbots: past, present, and future development [1.6019538204169677]
  The study traces key innovations leading to today's advanced conversational agents, such as ChatGPT and Google Bard. The paper highlights how natural language processing and machine learning have been integrated into modern chatbots for more sophisticated capabilities.
  arXiv  Detail & Related papers  (2024-02-04T05:01:38Z)
• Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models [76.99140362751787]
  We present NuInstruct, a novel dataset with 91K multi-view video-QA pairs across 17 subtasks. We also present BEV-InMLLM, an end-to-end method for efficiently deriving instruction-aware Bird's-Eye-View features.
  arXiv  Detail & Related papers  (2024-01-02T01:54:22Z)
• Large Language Models for Generative Information Extraction: A Survey [89.71273968283616]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. We present an extensive overview by categorizing these works in terms of various IE subtasks and techniques. We empirically analyze the most advanced methods and discover the emerging trend of IE tasks with LLMs.
  arXiv  Detail & Related papers  (2023-12-29T14:25:22Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest [62.63606958140248]
  Large Language Models (LLMs) often encounter challenges when dealing with intricate and knowledge-demanding queries in specific domains. This paper introduces a novel approach to enhance LLMs by effectively extracting the relevant knowledge from domain-specific textual sources. We train a knowledge miner, namely LLMiner, which autonomously extracts Question-Answer pairs from relevant documents.
  arXiv  Detail & Related papers  (2023-11-17T16:09:10Z)
• AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models [74.10293412011455]
  We propose AutoConv for synthetic conversation generation. Specifically, we formulate the conversation generation problem as a language modeling task. We finetune an LLM with a few human conversations to capture the characteristics of the information-seeking process.
  arXiv  Detail & Related papers  (2023-08-12T08:52:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.