Exploring ChatGPT for Next-generation Information Retrieval: Opportunities and Challenges
Yizheng Huang, Jimmy Huang
TL;DR
The paper addresses how ChatGPT and related GPT-X models influence information retrieval, identifying both opportunities and challenges. It surveys the evolution of pretrained language models, training regimes such as RLHF, interaction via prompts, and the role of GPT-4 in expanding capabilities. It details opportunities across information extraction, classification, document ranking, conversational search, and multimodal retrieval, while highlighting unresolved issues like hallucination, ethics, safety, and interpretability. The work emphasizes retrieval-augmented strategies and fairness considerations as essential for responsible deployment and outlines directions for future research and practical adoption.
Abstract
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.