Exploring ChatGPT for Next-generation Information Retrieval: Opportunities and Challenges
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Abstract (arXiv)
The rapid advancement of artificial intelligence (AI) has highlighted ChatGPT as a pivotal technology in the field of information retrieval (IR). Distinguished from its predecessors, ChatGPT offers significant benefits that have attracted the attention of both the industry and academic communities. While some view ChatGPT as a groundbreaking innovation, others attribute its success to the effective integration of product development and market strategies. The emergence of ChatGPT, alongside GPT-4, marks a new phase in Generative AI, generating content that is distinct from training examples and exceeding the capabilities of the prior GPT-3 model by OpenAI. Unlike the traditional supervised learning approach in IR tasks, ChatGPT challenges existing paradigms, bringing forth new challenges and opportunities regarding text quality assurance, model bias, and efficiency. This paper seeks to examine the impact of ChatGPT on IR tasks and offer insights into its potential future developments.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 54 of its 104 references that are in the map. First posted on arXiv 17 Feb 2024.
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54 of 104 references.
References · 54
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
- 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
- 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
- 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,813
38 more
- 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
- 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
- 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 849
- 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
- 2020Towards a Human-like Open-Domain Chatbotcited by 467
- 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 415
- 2015Recent Advances in Convolutional Neural Networkscited by 360
- 2022Text and Code Embeddings by Contrastive Pre-Trainingcited by 358
- 2023Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agentscited by 356
- 2018Graph Convolutional Networks for Text Classificationcited by 345
- 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 324
- 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 286
- 2021ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generationcited by 258
- 2023ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPTcited by 250
- 2022MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Textcited by 216
- 2022Re-Imagen: Retrieval-Augmented Text-to-Image Generatorcited by 208
- 2020A Survey of the State of Explainable AI for Natural Language Processingcited by 206
- 2019Multi-Stage Document Ranking with BERTcited by 200
- 2023Query2doc: Query Expansion with Large Language Modelscited by 146
- 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Searchcited by 137
- 2023Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulnesscited by 130
- 2022Retrieval-Augmented Multimodal Language Modelingcited by 125
- 2023A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasetscited by 123
- 2023Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicitycited by 86
- 2023Retrieving Multimodal Information for Augmented Generation: A Surveycited by 84
- 2021Towards Long-term Fairness in Recommendationcited by 77
- 2023Generative Recommendation: Towards Next-generation Recommender Paradigmcited by 69
- 2022Hypergraph Contrastive Collaborative Filteringcited by 63
- 2022FiD-Light: Efficient and Effective Retrieval-Augmented Text Generationcited by 63
- 2016A Comprehensive Survey on Cross-modal Retrievalcited by 62
- 2023How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Taskscited by 58
- 2023Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?cited by 54
- 2023Zero-shot Temporal Relation Extraction with ChatGPTcited by 52
- 2022Retrieval-Enhanced Machine Learningcited by 46
- 2023ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Modelcited by 40
- 2021Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learningcited by 35
- 2023ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphscited by 27
- 2020Neural Interactive Collaborative Filteringcited by 24
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