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ConTextual Mask Auto-Encoder for Dense Passage Retrieval

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Information Retrieval Researches

Codes and models for our information retrieval research papers.

Knowledge Computing and Service Group, Institute of Information Engineering, Chinese Academy of Sciences.

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tDRO: Task-level Distributionally Robust Optimization for Large Language Model-based Dense Retrieval. The tDRO (task-level Distributionally Robust Optimization) algorithm for Large Language Model-based Dense Retrieval (LLM-DR) fine-tuning, targeted at improving the universal domain generalization ability by end-to-end reweighting the data distribution of each task.

bowdpr: Drop your Decoder: Pre-training with Bag-of-Word Prediction for Dense Passage Retrieval. Bag-of-Word Prediction is a new encoder-only pre-training schema for dense retrieval targeted at efficiency and interpretability. (Accepted by SIGIR 2024)

CoT-MAE-qc: Query-as-context Pre-training for Dense Passage Retrieval. A simple yet effective pre-training scheme for single vector Dense Passage Retrieval. (Accepted by EMNLP 2023 Main Conference)

CoT-MAE: ConTextual Mask Auto-Encoder for Dense Passage Retrieval. CoT-MAE is a transformers based Mask Auto-Encoder pre-training architecture designed for Dense Passage Retrieval. (Accepted by AAAI 2022)

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