| Challenge: | XTR-style retrieval on top of trained Mono-T5 reranker is suboptimal for two-stage retrieval, arguing that it is sub-optimal. |
| Approach: | They propose a unified encoder-decoder architecture with a novel training regimen which enables the encoder representation to be used for retrieval and the decoder for re-ranking within a single unified model. |
| Outcome: | The proposed architecture outperforms ColBERT, XTR, and even serves as a superior reranker compared to the Mono-T5 re-ranker. |
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| Challenge: | Existing methods to train retrieval-based dialogue systems are suboptimal . existing methods to optimize retrieval and rerank modules are sub-optimal, causing sub-optimum performance. |
| Approach: | They propose a retrieval-based dialogue system with a fast retriever and a smart response reranker that combine the best of both worlds. |
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ED2LM: Encoder-Decoder to Language Model for Faster Document Re-ranking Inference (2022.findings-acl)
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Kai Hui, Honglei Zhuang, Tao Chen, Zhen Qin, Jing Lu, Dara Bahri, Ji Ma, Jai Gupta, Cicero Nogueira dos Santos, Yi Tay, Donald Metzler
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| Challenge: | Existing passage retrieval systems typically adopt a two-stage retrieve-then-rerank pipeline. |
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Retrieve Fast, Rerank Smart: Cooperative and Joint Approaches for Improved Cross-Modal Retrieval (2022.tacl-1)
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| Challenge: | Current approaches to cross-modal retrieval process text and visual input jointly . current approaches are pretrained from scratch and suffer from huge retrieval latency and inefficiency issues . |
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| Challenge: | BERT based ranking models have been successful on various information retrieval tasks, but they are prone to storage and network fetching latency. |
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Re2G: Retrieve, Rerank, Generate (2022.naacl-main)
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Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Naik, Pengshan Cai, Alfio Gliozzo
| Challenge: | Recent models such as RAG and REALM incorporate retrieval into conditional generation. |
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FIRST: Faster Improved Listwise Reranking with Single Token Decoding (2024.emnlp-main)
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Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image Retrieval (2022.naacl-main)
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| Challenge: | Contextual document embedding reranking is an efficient and efficient retrieval framework. |
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NAIL: Lexical Retrieval Indices with Efficient Non-Autoregressive Decoders (2023.emnlp-main)
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| Challenge: | Neural document rerankers require dedicated hardware for serving, which is costly and often not feasible. |
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