🔮 Project: Multi-Vector Retrieval (ColBERT)

[MotzWanted]

WHY Currently, VOD Training only complies with document-level embeddings. This represents each document with a single-vector representation, constraining the granularity of the contextual information captured.

ColBERT introduced a more complex interaction by encoding each passage into a matrix of token-level embeddings. During search, it further embeds every query into another matrix, allowing efficient passage retrieval that contextually matches the query using scalable vector-similarity operators.

The rich interactions enabled by ColBERT have been proven to surpass the quality of single-vector representation models. However, making it scale efficiently to large corpora is not trivial.

HOW The project will address the aforementioned goals through the following means:

Utilizing Fine-Grained Contextual Late Interaction:

• Leverage ColBERT's ability to encode queries and passages into sequences of token-level embeddings.
• Improve vod's on-disk data structures to handle 3-dimensional tensors with variable shapes (e.g., shape N x ? x H)
• Implement ColBERT's MaxSim operator in the loss layer
• Implement ColBERT's two-stage retrieval

Combine T5 Models with ColBERT:

• Benchmark ColT5 against ColBERT
• Benchmark the end-to-end search latency in search engine like Raffle.

Implement XTR: ContXextualized Token Retriever:

• Implement XTR loss
• Implement XTR one-stage retrieval

Refinements:

• Investigate Robust Multi-Hop Reasoning at Scale via Condensed Retrieval.
• Explore effective and efficient retrieval via Lightweight Late Interaction (e.g., PLAID)

WHAT The anticipated outcomes of this project include:

1. State-of-the-art retrieval for RAG models (T5 + XTR)
2. A scalable solution capable of handling large corpora without compromising efficiency.

References

• ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT (SIGIR'20).
• Relevance-guided Supervision for OpenQA with ColBERT (TACL'21).
• Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval (NeurIPS'21).
• ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction (NAACL'22).
• PLAID: An Efficient Engine for Late Interaction Retrieval (CIKM'22).
• Rethinking the Role of Token Retrieval in Multi-Vector Retrieval