Papers by Rangan Majumder
SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval (2022.emnlp-industry)
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Kun Zhou, Yeyun Gong, Xiao Liu, Wayne Xin Zhao, Yelong Shen, Anlei Dong, Jingwen Lu, Rangan Majumder, Ji-rong Wen, Nan Duan
| Challenge: | Existing methods for sapping negatives from large document pool suffer from the uninformative or false negative problem. |
| Approach: | They propose a method to sample negatives from a large document pool using a new sampling probability distribution. |
| Outcome: | The proposed method can be used to sample more ambiguous negatives on four public and one industry datasets. |
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation (2020.emnlp-main)
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Yaobo Liang, Nan Duan, Yeyun Gong, Ning Wu, Fenfei Guo, Weizhen Qi, Ming Gong, Linjun Shou, Daxin Jiang, Guihong Cao, Xiaodong Fan, Ruofei Zhang, Rahul Agrawal, Edward Cui, Sining Wei, Taroon Bharti, Ying Qiao, Jiun-Hung Chen, Winnie Wu, Shuguang Liu, Fan Yang, Daniel Campos, Rangan Majumder, Ming Zhou
| Challenge: | XGLUE provides a benchmark dataset to train large-scale cross-lingual pre-trained models . XCLUE provides 11 diversified tasks that cover both understanding and generation scenarios . |
| Approach: | They introduce a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora. |
| Outcome: | The proposed dataset is labeled in English and includes only natural language understanding tasks. |
Improving Text Embeddings with Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for obtaining text embeddings require complex training pipelines . authors leverage proprietary LLMs to generate diverse synthetic data for text embeds based on 93 languages . |
| Approach: | They propose a method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. |
| Outcome: | The proposed method achieves strong performance on competitive text embedding benchmarks without using any labeled data. |
SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval (2023.acl-long)
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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei
| Challenge: | SimLM uses a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training. |
| Approach: | They propose a simple yet effective pre-training method for dense passage retrieval that learns to compress the passage information into a dense vector through self-supervised pre-tuning. |
| Outcome: | The proposed method outperforms multi-vector approaches on large-scale passage retrieval datasets and shows significant improvements over baselines. |