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Found 6 papers, 4 papers with code
Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
Multi-vector retrieval models such as ColBERT [Khattab and Zaharia, 2020] allow token-level interactions between queries and documents, and hence achieve state of the art on many information retrieval benchmarks.
Multi-Vector Retrieval as Sparse Alignment
With sparsified unary saliences, we are able to prune a large number of query and document token vectors and improve the efficiency of multi-vector retrieval.
RARR: Researching and Revising What Language Models Say, Using Language Models
Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog.
Promptagator: Few-shot Dense Retrieval From 8 Examples
To amplify the power of a few examples, we propose Prompt-base Query Generation for Retriever (Promptagator), which leverages large language models (LLM) as a few-shot query generator, and creates task-specific retrievers based on the generated data.
Large Dual Encoders Are Generalizable Retrievers
With multi-stage training, surprisingly, scaling up the model size brings significant improvement on a variety of retrieval tasks, especially for out-of-domain generalization.
Ranked #9 on
Zero-shot Text Search
on BEIR
Finetuned Language Models Are Zero-Shot Learners
We show that instruction tuning -- finetuning language models on a collection of tasks described via instructions -- substantially improves zero-shot performance on unseen tasks.
Ranked #1 on
Question Answering
on OBQA
