Theme of inquiry
How should we design retrieval systems for effectiveness and robustness?
A question within its area, explored through 3 lines of inquiry below — each a family of specific questions the research asks.
63 specific questions
- Can knowledge graph structure be exploited for efficient multi-hop retrieval?
- Can graph-based retrieval with knowledge graphs scale to multi-hop reasoning?
- How do taxonomy-based retrieval scaffolds improve model performance at inference time?
- How do community-based summaries differ from retrieval-based traversal in knowledge graph RAG?
- Can knowledge graph structure alone generate sufficient training signals for domain reasoning?
- Can inference-time query decomposition replace pre-built knowledge graph structures?
- Can query-time logic graphs match the efficiency of pre-built knowledge graph indexing?
63 specific questions
- How do vector embeddings fail to capture task-relevant document relationships?
- Why do embedding-based retrieval systems fail on vocabulary mismatch?
- Can vector embeddings measure task relevance instead of semantic similarity?
- Why do semantic similarity and task relevance diverge in vector search results?
- Can embedding-based retrieval alone solve the causal relevance problem?
- What makes vector embeddings fail on single-hop semantic relevance queries?
- What mathematical limits constrain embedding-based retrieval systems?
120 specific questions
- Should retrieval be triggered always or only for difficult questions?
- What causes the retrieval-augmented generation to fail in practice?
- How should retrieval systems decide when to fetch new information?
- What role does document reranking play alongside decisions about whether to retrieve?
- Can long-context models replace retrieval-augmented generation systems?
- When does long-context LLM reasoning fail where structured retrieval succeeds?
- Why does search-augmented generation still not solve the verification problem?