no code implementations • 22 Oct 2023 • Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, Kai Hui
Recent studies show that large language models (LLMs) can be instructed to effectively perform zero-shot passage re-ranking, in which the results of a first stage retrieval method, such as BM25, are rated and reordered to improve relevance.
no code implementations • 23 May 2023 • Man Luo, Xin Xu, Zhuyun Dai, Panupong Pasupat, Mehran Kazemi, Chitta Baral, Vaiva Imbrasaite, Vincent Y Zhao
In-context learning (ICL), teaching a large language model (LLM) to perform a task with few-shot demonstrations rather than adjusting the model parameters, has emerged as a strong paradigm for using LLMs.
no code implementations • 2 Nov 2022 • Yujie Qian, Jinhyuk Lee, Sai Meher Karthik Duddu, Zhuyun Dai, Siddhartha Brahma, Iftekhar Naim, Tao Lei, Vincent Y. Zhao
With sparsified unary saliences, we are able to prune a large number of query and document token vectors and improve the efficiency of multi-vector retrieval.
5 code implementations • 20 Oct 2022 • Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei
We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation).
Ranked #1 on Multi-task Language Understanding on BBH-nlp
1 code implementation • 17 Oct 2022 • Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Y. Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, Kelvin Guu
Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog.
no code implementations • 23 Sep 2022 • Zhuyun Dai, Vincent Y. Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B. Hall, Ming-Wei Chang
To amplify the power of a few examples, we propose Prompt-base Query Generation for Retriever (Promptagator), which leverages large language models (LLM) as a few-shot query generator, and creates task-specific retrievers based on the generated data.
1 code implementation • 18 May 2022 • Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, Kelvin Guu
Our approach takes the text of any document and transforms it into a two-person dialog between the writer and an imagined reader: we treat sentences from the article as utterances spoken by the writer, and then use a dialog inpainter to predict what the imagined reader asked or said in between each of the writer's utterances.
2 code implementations • 15 Dec 2021 • Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y. Zhao, Yi Luan, Keith B. Hall, Ming-Wei Chang, Yinfei Yang
With multi-stage training, surprisingly, scaling up the model size brings significant improvement on a variety of retrieval tasks, especially for out-of-domain generalization.
Ranked #9 on Zero-shot Text Search on BEIR
1 code implementation • NAACL 2021 • Luyu Gao, Zhuyun Dai, Jamie Callan
Classical information retrieval systems such as BM25 rely on exact lexical match and carry out search efficiently with inverted list index.
1 code implementation • 21 Jan 2021 • Luyu Gao, Zhuyun Dai, Jamie Callan
Pre-trained deep language models~(LM) have advanced the state-of-the-art of text retrieval.
1 code implementation • 20 Jan 2021 • HongChien Yu, Zhuyun Dai, Jamie Callan
Most research on pseudo relevance feedback (PRF) has been done in vector space and probabilistic retrieval models.
3 code implementations • 3 Nov 2020 • Chenyan Xiong, Zhenghao Liu, Si Sun, Zhuyun Dai, Kaitao Zhang, Shi Yu, Zhiyuan Liu, Hoifung Poon, Jianfeng Gao, Paul Bennett
Neural rankers based on deep pretrained language models (LMs) have been shown to improve many information retrieval benchmarks.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Zhenghao Liu, Chenyan Xiong, Zhuyun Dai, Si Sun, Maosong Sun, Zhiyuan Liu
With the epidemic of COVID-19, verifying the scientifically false online information, such as fake news and maliciously fabricated statements, has become crucial.
no code implementations • 21 Jul 2020 • Luyu Gao, Zhuyun Dai, Jamie Callan
Deep language models such as BERT pre-trained on large corpus have given a huge performance boost to the state-of-the-art information retrieval ranking systems.
1 code implementation • 23 May 2020 • Shuo Zhang, Zhuyun Dai, Krisztian Balog, Jamie Callan
We propose to generate natural language summaries as answers to describe the complex information contained in a table.
no code implementations • 29 Apr 2020 • Luyu Gao, Zhuyun Dai, Tongfei Chen, Zhen Fan, Benjamin Van Durme, Jamie Callan
This paper presents CLEAR, a retrieval model that seeks to complement classical lexical exact-match models such as BM25 with semantic matching signals from a neural embedding matching model.
no code implementations • EMNLP 2020 • Luyu Gao, Zhuyun Dai, Jamie Callan
Recent innovations in Transformer-based ranking models have advanced the state-of-the-art in information retrieval.
2 code implementations • 23 Oct 2019 • Zhuyun Dai, Jamie Callan
When applied to passages, DeepCT-Index produces term weights that can be stored in an ordinary inverted index for passage retrieval.
1 code implementation • 22 May 2019 • Zhuyun Dai, Jamie Callan
Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations.
Ranked #5 on Ad-Hoc Information Retrieval on TREC Robust04
no code implementations • 22 Oct 2018 • Salvador Medina, Zhuyun Dai, Yingkai Gao
In this paper, we propose a family of voting-based methods to aggregate frame-wise geolocation results which boost the video geolocation result.
no code implementations • 27 Sep 2018 • Mary Arpita Pyreddy, Varshini Ramaseshan, Narendra Nath Joshi, Zhuyun Dai, Chenyan Xiong, Jamie Callan, Zhiyuan Liu
This paper studies the consistency of the kernel-based neural ranking model K-NRM, a recent state-of-the-art neural IR model, which is important for reproducible research and deployment in the industry.
no code implementations • WSDM 2018 2018 • Zhuyun Dai, Chenyan Xiong, Jamie Callan, Zhiyuan Liu
This paper presents Conv-KNRM, a Convolutional Kernel-based Neural Ranking Model that models n-gram soft matches for ad-hoc search.
1 code implementation • 20 Jun 2017 • Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, Russell Power
Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score.