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Found 34 papers, 27 papers with code
Measuring Cross-lingual Transfer in Bytes
We also found evidence that this transfer is not related to language contamination or language proximity, which strengthens the hypothesis that the model also relies on language-agnostic knowledge.
Lissard: Long and Simple Sequential Reasoning Datasets
Language models are now capable of solving tasks that require dealing with long sequences consisting of hundreds of thousands of tokens.
ExaRanker-Open: Synthetic Explanation for IR using Open-Source LLMs
ExaRanker recently introduced an approach to training information retrieval (IR) models, incorporating natural language explanations as additional labels.
InRanker: Distilled Rankers for Zero-shot Information Retrieval
Despite multi-billion parameter neural rankers being common components of state-of-the-art information retrieval pipelines, they are rarely used in production due to the enormous amount of compute required for inference.
INACIA: Integrating Large Language Models in Brazilian Audit Courts: Opportunities and Challenges
This paper introduces INACIA (Instru\c{c}\~ao Assistida com Intelig\^encia Artificial), a groundbreaking system designed to integrate Large Language Models (LLMs) into the operational framework of Brazilian Federal Court of Accounts (TCU).
InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval
Our toolkit not only reproduces the InPars method and partially reproduces Promptagator, but also provides a plug-and-play functionality allowing the use of different LLMs, exploring filtering methods and finetuning various reranker models on the generated data.
Automated computed tomography and magnetic resonance imaging segmentation using deep learning: a beginner's guide
Medical image segmentation is an increasingly popular area of research in medical imaging processing and analysis.
Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams
The present study aims to explore the capabilities of Language Models (LMs) in tackling high-stakes multiple-choice tests, represented here by the Exame Nacional do Ensino M\'edio (ENEM), a multidisciplinary entrance examination widely adopted by Brazilian universities.
NeuralMind-UNICAMP at 2022 TREC NeuCLIR: Large Boring Rerankers for Cross-lingual Retrieval
This paper reports on a study of cross-lingual information retrieval (CLIR) using the mT5-XXL reranker on the NeuCLIR track of TREC 2022.
ExaRanker: Explanation-Augmented Neural Ranker
Recent work has shown that inducing a large language model (LLM) to generate explanations prior to outputting an answer is an effective strategy to improve performance on a wide range of reasoning tasks.
InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval
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.
Visconde: Multi-document QA with GPT-3 and Neural Reranking
This paper proposes a question-answering system that can answer questions whose supporting evidence is spread over multiple (potentially long) documents.
In Defense of Cross-Encoders for Zero-Shot Retrieval
We find that the number of parameters and early query-document interactions of cross-encoders play a significant role in the generalization ability of retrieval models.
NeuralSearchX: Serving a Multi-billion-parameter Reranker for Multilingual Metasearch at a Low Cost
We compare our system with Microsoft's Biomedical Search and show that our design choices led to a much cost-effective system with competitive QPS while having close to state-of-the-art results on a wide range of public benchmarks.
Open-source tool for Airway Segmentation in Computed Tomography using 2.5D Modified EfficientDet: Contribution to the ATM22 Challenge
Airway segmentation in computed tomography images can be used to analyze pulmonary diseases, however, manual segmentation is labor intensive and relies on expert knowledge.
mRobust04: A Multilingual Version of the TREC Robust 2004 Benchmark
Robust 2004 is an information retrieval benchmark whose large number of judgments per query make it a reliable evaluation dataset.
MonoByte: A Pool of Monolingual Byte-level Language Models
The zero-shot cross-lingual ability of models pretrained on multilingual and even monolingual corpora has spurred many hypotheses to explain this intriguing empirical result.
Induced Natural Language Rationales and Interleaved Markup Tokens Enable Extrapolation in Large Language Models
Our experimental results show that generating step-by-step rationales and introducing marker tokens are both required for effective extrapolation.
A Boring-yet-effective Approach for the Product Ranking Task of the Amazon KDD Cup 2022
In this work we describe our submission to the product ranking task of the Amazon KDD Cup 2022.
No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
This has made distilled and dense models, due to latency constraints, the go-to choice for deployment in real-world retrieval applications.
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Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task
Recent work has shown that language models scaled to billions of parameters, such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios.
On the ability of monolingual models to learn language-agnostic representations
Pretrained multilingual models have become a de facto default approach for zero-shot cross-lingual transfer.
mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset
In this work, we present mMARCO, a multilingual version of the MS MARCO passage ranking dataset comprising 13 languages that was created using machine translation.
A cost-benefit analysis of cross-lingual transfer methods
An effective method for cross-lingual transfer is to fine-tune a bilingual or multilingual model on a supervised dataset in one language and evaluating it on another language in a zero-shot manner.
Yes, BM25 is a Strong Baseline for Legal Case Retrieval
We describe our single submission to task 1 of COLIEE 2021.
Can questions summarize a corpus? Using question generation for characterizing COVID-19 research
What are the latent questions on some textual data?
PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data
In natural language processing (NLP), there is a need for more resources in Portuguese, since much of the data used in the state-of-the-art research is in other languages.
Lite Training Strategies for Portuguese-English and English-Portuguese Translation
Despite the widespread adoption of deep learning for machine translation, it is still expensive to develop high-quality translation models.
Electricity Theft Detection with self-attention
In this work we propose a novel self-attention mechanism model to address electricity theft detection on an imbalanced realistic dataset that presents a daily electricity consumption provided by State Grid Corporation of China.
Hippocampus Segmentation on Epilepsy and Alzheimer's Disease Studies with Multiple Convolutional Neural Networks
We test this methodology alongside other recent deep learning methods, in two domains: The HarP test set and an in-house epilepsy dataset, containing hippocampus resections, named HCUnicamp.
Portuguese Named Entity Recognition using BERT-CRF
Recent advances in language representation using neural networks have made it viable to transfer the learned internal states of a trained model to downstream natural language processing tasks, such as named entity recognition (NER) and question answering.
Extended 2D Consensus Hippocampus Segmentation
Segmentation done by experts is considered to be a gold-standard when evaluating automated methods, buts it is a time consuming and arduos task, requiring specialized personnel.
Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver standard Masks
Our use of silver standard masks reduced the cost of manual annotation, decreased inter-intra-rater variability, and avoided CNN segmentation super-specialization towards one specific manual annotation guideline that can occur when gold standard masks are used.
