The recent advancements in the field of information retrieval and summarization are marked by a significant shift towards leveraging the capabilities of Large Language Models (LLMs). These models are being increasingly utilized to enhance various stages of the retrieval and summarization processes, from initial ranking to post-ranking and query-focused summarization. Innovations such as agentic rerankers that emulate human cognitive processes, domain-specific guided summarization, and data fusion techniques using synthetic query variants are pushing the boundaries of what is possible in these areas. Notably, the integration of LLMs into post-ranking stages and the distillation of LLM capabilities into more compact models like BERT are addressing practical challenges related to computational efficiency and resource constraints. Additionally, the development of specialized datasets and frameworks for fine-tuning and inference of transformer-based models is making these advanced techniques more accessible and scalable. Overall, the field is moving towards more nuanced, context-aware, and efficient systems that can better serve the needs of users in diverse environments.
Enhancing Information Retrieval and Summarization with Large Language Models
Sources
JudgeRank: Leveraging Large Language Models for Reasoning-Intensive Reranking
Learning to Rank Salient Content for Query-focused Summarization
LLM4PR: Improving Post-Ranking in Search Engine with Large Language Models
Domain-specific Guided Summarization for Mental Health Posts
Data Fusion of Synthetic Query Variants With Generative Large Language Models
AmazonQAC: A Large-Scale, Naturalistic Query Autocomplete Dataset
Best Practices for Distilling Large Language Models into BERT for Web Search Ranking
Self-Calibrated Listwise Reranking with Large Language Models
Lightning IR: Straightforward Fine-tuning and Inference of Transformer-based Language Models for Information Retrieval