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 be used to train a retriever. However, InPars and, more
recently, Promptagator, rely on proprietary LLMs such as GPT-3 and FLAN to
generate such datasets. In this work we introduce InPars-v2, a dataset
generator that uses open-source LLMs and existing powerful rerankers to select
synthetic query-document pairs for training. A simple BM25 retrieval pipeline
followed by a monoT5 reranker finetuned on InPars-v2 data achieves new
state-of-the-art results on the BEIR benchmark. To allow researchers to further
improve our method, we open source the code, synthetic data, and finetuned
models: this https URL

通过使用开源的大型语言模型和现有强大的重排序器，InPars-v2 提出了一种用于生成训练数据的合成查询 - 文档对的数据集生成器，并通过简单的 BM25 检索管道和使用 InPars-v2 数据微调的 monoT5 重排序器，取得了 BEIR 基准测试的最新最优结果。

InPars-v2: 信息检索高效数据集生成器的大型语言模型

InPars-v2: Large Language Models as Efficient Dataset Generators for Information Retrieval

This paper introduces the problem of Fine-grained Incident Video Retrieval
(FIVR). Given a query video, the objective is to retrieve all associated
videos, considering several types of associations that range from duplicate
videos to videos from the same incident. FIVR offers a single framework that
contains several retrieval tasks as special cases. To address the benchmarking
needs of all such tasks, we construct and present a large-scale annotated video
dataset, which we call FIVR-200K, and it comprises 225,960 videos. To create
the dataset, we devise a process for the collection of YouTube videos based on
major news events from recent years crawled from Wikipedia and deploy a
retrieval pipeline for the automatic selection of query videos based on their
estimated suitability as benchmarks. We also devise a protocol for the
annotation of the dataset with respect to the four types of video associations
defined by FIVR. Finally, we report the results of an experimental study on the
dataset comparing five state-of-the-art methods developed based on a variety of
visual descriptors, highlighting the challenges of the current problem.

本文介绍了细粒度事件视频检索的问题，提出了一个包含多个检索任务的单一框架 FIVR，以及一个包含 225,960 个视频的大规模标注视频数据集 FIVR-200K，并报告了对该数据集进行的实验研究的结果，比较了五种最先进的方法，突出了当前问题的挑战。