Rodrigo Nogueira
8 papers in the PaperMetrix corpus
Papers by this author
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Investigating the Limitations of Transformers with Simple Arithmetic Tasks
2021 · arXiv (Cornell University)
The ability to perform arithmetic tasks is a remarkable trait of human intelligence and might form a critical component of more complex reasoning tasks. In this work, we investigate if the surface form of a …
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InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
2023 · arXiv (Cornell University)
Recently, InPars introduced a method to efficiently use large language models (LLMs) in information retrieval tasks: via few-shot examples, an LLM is induced to generate relevant queries for documents. These synthetic query-document pairs can then …
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Passage Re-ranking with BERT
2019 · arXiv (Cornell University)
Recently, neural models pretrained on a language modeling task, such as ELMo (Peters et al., 2017), OpenAI GPT (Radford et al., 2018), and BERT (Devlin et al., 2018), have achieved impressive results on various natural …
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Document Expansion by Query Prediction
2019 · arXiv (Cornell University)
One technique to improve the retrieval effectiveness of a search engine is to expand documents with terms that are related or representative of the documents' content.From the perspective of a question answering system, this might …
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Task-Oriented Query Reformulation with Reinforcement Learning
2017
Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, …
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Multi-Stage Document Ranking with BERT
2019 · arXiv (Cornell University)
The advent of deep neural networks pre-trained via language modeling tasks has spurred a number of successful applications in natural language processing. This work explores one such popular model, BERT, in the context of document …
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Pretrained Transformers for Text Ranking: BERT and Beyond
2020 · arXiv (Cornell University)
The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the …
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Document Ranking with a Pretrained Sequence-to-Sequence Model
2020
This work proposes the use of a pretrained sequence-to-sequence model for document ranking. Our approach is fundamentally different from a commonly adopted classificationbased formulation based on encoder-only pretrained transformer architectures such as BERT. We show …