Can LLMs generate diverse query expansions?
QA-Expand: Multi-Question Answer Generation for Enhanced Query Expansion in Information Retrieval
February 13, 2025
https://arxiv.org/pdf/2502.08557This paper introduces QA-Expand, a new technique for improving search results by expanding the initial search query. Instead of directly adding related terms, QA-Expand uses an LLM to generate relevant questions from the initial query and then generates "pseudo-answers" to those questions, treating them like extra information. A feedback loop refines these answers, keeping only the most relevant parts. Finally, these refined answers are combined with the original query to create a more comprehensive search query.
Key points for LLM-based multi-agent systems:
- Multi-agent analogy: The question generation and answer generation steps can be seen as separate agents collaborating to enrich the query's understanding.
- Feedback loop refinement: The feedback mechanism acts as a coordinating agent, evaluating and refining the contributions of other agents (question/answer generators).
- Decentralized information gathering: Each generated question explores a different facet of the original query, similar to how multiple agents might gather information from different sources.
- Emergent behavior: The combined, refined search query represents a more nuanced understanding than any individual question or answer, an example of emergent behavior from multi-agent interaction.
- Potential for complex coordination: This framework opens possibilities for more complex multi-agent coordination in web development, with agents specializing in different aspects of information processing and retrieval.