LLM-Based Query Expansion and Collaborative Document Ranking for Open-Domain Search
Keywords:
open-domain search; query expansion; collaborative ranking; large language models; information retrieval; socio-technical systems; governance; robustness; fairness; sustainabilityAbstract
Open-domain search increasingly depends on systems that combine large language models with retrieval and ranking pipelines. This paper examines LLM-based query expansion and collaborative document ranking as coupled socio-technical infrastructure rather than isolated model improvements. It argues that query expansion changes the interface between user intent and indexed evidence, while collaborative ranking distributes judgment across multiple model roles and retrieval stages. The paper develops a system-level account of architecture, trade-offs, deployment, sustainability, robustness, fairness, governance, and policy. It reviews lexical and dense retrieval foundations, then analyzes how LLM-generated expansions can improve recall but introduce drift, latency, and accountability concerns. It positions multi-role reranking as a coordination mechanism that can improve relevance by separating query interpretation, evidence gathering, critique, and final ordering, yet it also creates new dependencies on prompt design, model routing, and evaluation infrastructure. The discussion considers compute cost, caching, distillation, edge deployment, and human oversight. It also examines fairness risks when expansion vocabularies reflect uneven linguistic and cultural representation, and when ranking feedback loops amplify popularity. Evaluation must move beyond static benchmarks to include adversarial queries, multilingual coverage, provenance, and longitudinal monitoring. Governance proposals include model cards, audit trails, retrieval provenance, contestability, and staged deployment. The conclusion is that LLM-mediated search should be treated as critical information infrastructure whose legitimacy depends on transparent coordination, sustainable operations, and inclusive evaluation.
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