Search Results for author:
Found 8 papers, 8 papers with code
NoMIRACL: Knowing When You Don't Know for Robust Multilingual Retrieval-Augmented Generation
We measure LLM robustness using two metrics: (i) hallucination rate, measuring model tendency to hallucinate an answer, when the answer is not present in passages in the non-relevant subset, and (ii) error rate, measuring model inaccuracy to recognize relevant passages in the relevant subset.
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.
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.
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.
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.
Ranked #1 on
Citation Prediction
on SciDocs (BEIR)
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.
InPars: Data Augmentation for Information Retrieval using Large Language Models
In this work, we harness the few-shot capabilities of large pretrained language models as synthetic data generators for IR tasks.
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.
