5 accepted papers
Existing passage retrieval systems typically adopt a two-stage retrieve-then-rerank pipeline. To obtain an effective reranking model, many prior works have focused on improving the model architectures, such as leveraging powerful pretrained large language models (LLM) and designing better objective…
We develop and evaluate multilingual scientific documents similarity measurement models in this work. Such models can be used to find related papers in different languages, which can help multilingual researchers find and explore papers more efficiently. We propose the first multilingual scientific…
Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other retrieval tasks where supervision is limited, with the implicit assumption that it is possible to generalize from one task to all the rest. However, t…
It has been shown that dual encoders trained on one domain often fail to generalize to other domains for retrieval tasks. One widespread belief is that the bottleneck layer of a dual encoder, where the final score is simply a dot-product between a query vector and a passage vector, is too limited co…
We provide the first exploration of sentence embeddings from text-to-text transformers (T5) including the effects of scaling up sentence encoders to 11B parameters. Sentence embeddings are broadly useful for language processing tasks. While T5 achieves impressive performance on language tasks, it is…