What is the role of generative models in IR?

Generative models play a significant role in improving how information retrieval (IR) systems understand, process, and deliver results. Unlike traditional IR methods that rely on keyword matching or statistical relevance, generative models enable systems to interpret user intent, generate context-aware responses, and refine search outputs. For example, models like BERT or T5 can analyze the semantic meaning of queries and documents, allowing IR systems to retrieve results based on conceptual relevance rather than just exact word matches. This is particularly useful for handling ambiguous queries or expanding search criteria to include synonyms or related concepts. Generative models also enable dynamic content creation, such as summarizing documents or generating answers directly, which enhances the user experience.

One key application of generative models in IR is query understanding and expansion. When a user submits a search query, generative models can rephrase or expand it to capture underlying intent. For instance, a query like “ways to cool a room” might be transformed into “methods for reducing indoor temperature without AC,” improving the likelihood of retrieving relevant results. Models like GPT can generate alternative phrasings or suggest related terms, helping IR systems overcome vocabulary mismatches between user queries and indexed content. Another use case is document summarization, where models like BART or PEGASUS generate concise summaries of long texts, allowing users to quickly assess relevance without reading entire documents. These capabilities make IR systems more efficient and user-friendly.

However, integrating generative models into IR systems introduces challenges. First, computational resources and latency can be a barrier, as generating text in real-time requires significant processing power. For example, deploying a large model like GPT-3 for query expansion might be impractical for low-latency applications. Second, biases in training data can lead to skewed or inappropriate outputs, requiring careful filtering and fine-tuning. Additionally, generative models may produce plausible-sounding but inaccurate information, necessitating mechanisms to verify factual correctness. Developers must balance the benefits of generative capabilities with these trade-offs, often by combining generative models with traditional retrieval methods or using smaller, optimized models tailored to specific tasks.