Overview
Information retrieval systems have evolved from simple keyword matching to sophisticated neural architectures, yet they still typically apply the same approach regardless of query type or content characteristics. This “one-size-fits-all” paradigm fundamentally limits effectiveness across the diverse spectrum of information needs that users express.
Our research in this direction focuses on creating retrieval systems that dynamically adapt their representations and retrieval strategies based on the nature of the information need, the characteristics of the content, and the context of the search. This adaptive approach aims to bring together the complementary strengths of different retrieval paradigms—sparse (BM25, lexical), dense (embedding-based), and hybrid approaches—creating systems that can intelligently select the most appropriate strategy for each unique situation.
Key Research Challenges
Representation Granularity
Current retrieval models either use document-level representations (single vector per document) or fixed token-level representations (multiple vectors per document). Neither approach is optimal for all content types. Some documents contain diverse topics requiring fine-grained representation, while others are focused and could be efficiently represented with fewer vectors. How do we create models that adaptively adjust their representation granularity based on content?
Hybrid Retrieval Optimization
Different retrieval approaches excel at different types of queries. Lexical retrievers perform well when exact terminology matters, while dense retrievers capture semantic relationships. How do we develop theoretically grounded frameworks for optimally combining these approaches based on query characteristics?
Collaborative Information Discovery
Complex information needs often require exploring diverse aspects of a topic. Current retrieval methods tend to focus on maximizing relevance to a single interpretation of the query, missing important subtopics or alternative perspectives. How can multiple retrieval approaches work together to provide comprehensive coverage of complex topics?
Theoretical Understanding
Despite empirical advances, there remains a significant gap in theoretical understanding of why neural retrieval models succeed or fail. What mathematical principles govern the behavior of these systems, and how can this understanding guide more principled design?
Research Questions
Our work explores several interrelated research questions:
- How might retrieval systems dynamically adjust their representation granularity based on content complexity and information need?
- What theoretical frameworks could help us understand the complementary strengths of different retrieval paradigms and guide their optimal combination?
- How can we develop collaborative frameworks where multiple approaches work together to explore information spaces more effectively?
- What mathematical properties of embedding spaces dictate retrieval performance, and how can understanding these properties lead to more robust systems?
- How can retrieval systems efficiently adapt to different query types without requiring prohibitive computational resources?
- What mechanisms would enable retrieval systems to dynamically reconfigure their strategies based on user feedback and interaction patterns?
- How can systems balance the trade-offs between precision, diversity, and efficiency when adapting their retrieval strategies?
Broader Directions
Our research encompasses several broader directions:
Adaptive Multi-Vector Representations
Exploring approaches that dynamically determine how many vectors should represent different parts of a document based on content complexity and information density.
Theoretically-Grounded Hybrid Retrieval
Developing probabilistic frameworks that formally characterize when and how to combine different retrieval signals with provable performance guarantees.
Collaborative and Multi-Agent Retrieval
Creating systems where multiple retrieval “agents” with different strategies work together to explore and rank information more effectively than any single approach.
Geometric and Topological Analysis of Embedding Spaces
Investigating the mathematical properties of embedding spaces that impact retrieval performance, establishing formal relationships between embedding characteristics and retrieval quality.
Cross-Modal Representation and Retrieval
Developing unified representation spaces that effectively capture relationships across modalities (text, images, video, audio) for more comprehensive information access.
By advancing research in these directions, we aim to create retrieval systems that can intelligently adapt to the specific demands of each information need, dramatically improving both effectiveness and efficiency across diverse search scenarios.