Zero-Shot Listwise Document Reranking with a Large Language Model
暂无分享,去创建一个
[1] Zekai Chen,et al. Language Models are Few-shot Learners for Prognostic Prediction , 2023, ArXiv.
[2] Eric Nyberg,et al. InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers , 2023, ArXiv.
[3] Jimmy J. Lin,et al. Precise Zero-Shot Dense Retrieval without Relevance Labels , 2022, ACL.
[4] Nandan Thakur,et al. Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages , 2022, ArXiv.
[5] Xuanhui Wang,et al. RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses , 2022, SIGIR.
[6] Keith B. Hall,et al. Promptagator: Few-shot Dense Retrieval From 8 Examples , 2022, ICLR.
[7] Rodrigo Nogueira,et al. InPars: Unsupervised Dataset Generation for Information Retrieval , 2022, SIGIR.
[8] Devendra Singh Sachan,et al. Improving Passage Retrieval with Zero-Shot Question Generation , 2022, EMNLP.
[9] Jimmy J. Lin,et al. Toward Best Practices for Training Multilingual Dense Retrieval Models , 2022, ACM Trans. Inf. Syst..
[10] Ryan J. Lowe,et al. Training language models to follow instructions with human feedback , 2022, NeurIPS.
[11] Edouard Grave,et al. Unsupervised Dense Information Retrieval with Contrastive Learning , 2021, Trans. Mach. Learn. Res..
[12] Alexander M. Rush,et al. Multitask Prompted Training Enables Zero-Shot Task Generalization , 2021, ICLR.
[13] Quoc V. Le,et al. Finetuned Language Models Are Zero-Shot Learners , 2021, ICLR.
[14] Rodrigo Nogueira,et al. mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset , 2021, 2108.13897.
[15] Iryna Gurevych,et al. BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models , 2021, NeurIPS Datasets and Benchmarks.
[16] Luyu Gao,et al. Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline , 2021, ECIR.
[17] Nicola De Cao,et al. KILT: a Benchmark for Knowledge Intensive Language Tasks , 2020, NAACL.
[18] Jimmy J. Lin,et al. In-Batch Negatives for Knowledge Distillation with Tightly-Coupled Teachers for Dense Retrieval , 2021, REPL4NLP.
[19] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[20] Bhaskar Mitra,et al. Overview of the TREC 2019 deep learning track , 2020, ArXiv.
[21] Jimmy J. Lin,et al. Document Ranking with a Pretrained Sequence-to-Sequence Model , 2020, FINDINGS.
[22] Colin Raffel,et al. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..
[23] Nick Craswell,et al. O VERVIEW OF THE TREC 2019 DEEP LEARNING TRACK , 2020 .
[24] Jimmy J. Lin,et al. Multi-Stage Document Ranking with BERT , 2019, ArXiv.
[25] Kyunghyun Cho,et al. Passage Re-ranking with BERT , 2019, ArXiv.
[26] Jason Weston,et al. Reading Wikipedia to Answer Open-Domain Questions , 2017, ACL.
[27] Hugo Zaragoza,et al. The Probabilistic Relevance Framework: BM25 and Beyond , 2009, Found. Trends Inf. Retr..