文档智能：OCR+Rocketqa+layoutxlm ＜Rocketqa＞

此次梳理Rocketqa，个人认为该篇文件讲述的是段落搜索的改进点，关于其框架：粗检索 + 重排序----（dual-encoder architecture），讲诉不多，那是另外的文章；

之前根据文档智能功能，粗略过了一遍。

文档智能：OCR+Rocketqa+layoutxlm＜LayoutLMv2＞

最近在看RAG相关内容，提到了检索排序，故而重新梳理。如有不足或错误之处，欢迎感谢指正。

记录如下：

RocketQA是一种优化训练方法，用于密集段落检索（Dense Passage Retrieval，DPR），以支持开放域问答（Open-Domain Question Answering，ODQA）系统。

1. Abstract & Introduction

It is difficult to effectively train a dual-encoder for dense passage retrieval due to the following three major challenges:

First, there exists the discrepancy between training and inference for the dual-encoder retriever.
During inference, the retriever needs to identify positive (or relevant) passages for each question from a large collection containing millions of candidates.
However, during training, the model is learned to estimate the probabilities of positive passages in a small candidate set for each question, due to the limited memory of a single GPU (or other device).

To reduce such a discrepancy, previous work tried to design specific mechanisms for selecting a few hard negatives from the top-k retrieved candidates. However, it suffers from the false negative issue due to the following challenge.

Second, there might be a large number of unlabeled positives.

Third, it is expensive to acquire large-scale training data for open-domain QA.

采用的一系列优化策略：跨批次负采样（Cross-batch Negatives）、去噪的强负例采样（Denoised Hard Negatives）和数据增强（Data Augmentation）等。

用于解决训练过程中负例样本不足，和，存在大量错误负例样本的问题。

First, RocketQA introduces cross-batch negatives. Comparing to in-batch negatives, it increases the number of available negatives for each question during training, and alleviates the discrepancy between training and inference.

Second, RocketQA introduces denoised hard negatives. It aims to remove false negatives from the top-ranked results retrieved by a retriever, and derive more reliable hard negatives.

Third, RocketQA leverages large-scale unsupervised data “labeled” by a cross-encoder (as shown in Figure1b) for data augmentation.

Though inefficient, the cross-encoder architecture has been found to be more capable than the dual-encoder architecture in both theory and practice.

Therefore, we utilize a cross-encoder to generate high quality pseudo labels for unlabeled data which are used to train the dual-encoder retriever.

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2. Related work

2.1 Passage retrieval for open-domain QA

Recently, researchers have utilized deep learning to improve traditional passage retrievers, including:

document expansions,
question expansions,
term weight estimation.

Different from the above term-based approaches, dense passage retrieval has been proposed to represent both questions and documents as dense vectors (i.e., embeddings), typically in a dual-encoder architecture (as shown in Figure 1a).

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Existing approaches can be divided into two categories:
(1) self-supervised pre-training for retrieval.
(2) fine-tuning pre-trained language models on labeled data.

Our work follows the second class of approaches, which show better performance with less cost.

2.2 Passage re-ranking for open-domain QA

Based on the retrieved passages from a first-stage retriever, BERT-based rerankers have recently been applied to retrieval-based question answering and search-related tasks, and yield substantial improvements over the traditional methods.
基于从第一阶段检索器检索到的段落，BERT-based（基于BERT的）重排器最近被应用于基于检索的问答系统和搜索相关任务，相较于传统方法，取得了显著的改进。

Although effective to some extent, these rankers employ the cross-encoder architecture (as shown in Figure 1b) that is impractical to be applied to all passages in a corpus with respect to a question.
尽管在某种程度上是有效的，但这些排序器采用了交叉编码器架构（如图1b所示），这对于应用于语料库中与问题有关的所有段落是不切实际的。

The re-rankers with light weight interaction based on the representations of dense retrievers have been studied. However, these techniques still rely on a separate retriever which provides candidates and representations.
已经研究了基于密集检索器表示且具有轻量级交互的重排器。然而，这些技术仍然依赖于一个单独的检索器来提供候选结果和表示。

As a comparison, we focus on developing dual-encoder based retrievers.

3. Approach

3.1 Task Description

The task of open-domain QA is described as follows.
Given a natural language question, a system is required to answer it based on a large collection of documents.

Let $C$ denote the corpus, consisting of $N$ documents.

We split the $N$ documents into $M$ passages, denoted by $p_{1}$ , $p_{2}$ , …, $p_{M}$ ,

where each passage $p_{i}$ can be viewed as an $l$ -length sequence of tokens $p_{i}^{(1)}$ , $p_{i}^{(2)}$ , …, $p_{i}^{(l)}$ .

Given a question $q$ , the task is to find a passage $p_{i}$ among the $M$ candidates,

and extract a span $p_{i}^{(s)}$ , $p_{i}^{(s+1)}$ , …, $p_{i}^{(e)}$ from $p_{i}$ that can answer the question.

In this paper, we mainly focus on developing a dense retriever to retrieve the passages that contain the answer.

每个段落的长度 $l$ 是同一个数值吗？

见4.1.3：

4.1.3 Implementation Details

1. Maximal length

We set the maximum length of questions and passages as 32 and 128, respectively.

3.2 The Dual-Encoder Architecture

We develop our passage retriever based on the typical dual-encoder architecture, as illustrated in Figure 1a.

First, a dense passage retriever uses an encoder $E_{p}(·)$ to obtain the $d$ -dimensional real-valued vectors (a.k.a., embedding) of passages.

Then, an index of passage embeddings is built for retrieval.

At query time, another encoder $E_{q}(·)$ is applied to embed the input question to a $d$ -dimensional real-valued vector, and $k$ passages whose embeddings are the closest to the question’s will be retrieved.

The similarity between the question $q$ and a candidate passage $p$ can be computed as the dot product of their vectors:
在这里插入图片描述

In practice, the separation of question encoding and passage encoding is desirable, so that the dense representations of all passages can be precomputed for efficient retrieval.
在实践中，将问题编码和段落编码分离是理想的做法，因为这样可以先预先计算出所有段落的密集表示，从而实现高效的检索。

Here, we adopt two independent neural networks initialized from pre-trained LMs for the two encoders $E_{q}(·)$ and $E_{p}(·)$ separately,
在这里，我们分别为两个编码器 Eq(·) 和 Ep(·) 采用了两个从预训练语言模型（LMs）初始化的独立神经网络，

and take the representations at the first token (e.g., [CLS] symbol in BERT) as the output for encoding.
并取第一个标记（例如，在BERT中的[CLS]符号）的表示作为编码的输出。

为什么使用[CLS]符号）的表示作为编码的输出，简单解释的话，是BERT使用的是transformer结构，而一句话的开始的标记[CLS]能够“兼顾”整句话的含义。

详细可以看链接：
https://blog.csdn.net/sdsasaAAS/article/details/142926242
https://blog.csdn.net/weixin_45947938/article/details/144232649

3.2.1 Training

Formally, given a question $q_{i}$ together with its positive passage $p_{i}^+$ and $m$ negative passages $\left\{p_{i, j}^-\right\}_{j=1}^m$ , we minimize the loss function:

在这里插入图片描述

where we aim to optimize the negative log likelihood of the positive passage against a set of $m$ negative passages.

Ideally, we should take all the negative passages in the whole collection into consideration in Equation 2.

However, it is computationally infeasible to consider a large number of negative samples for a question, and hence $m$ is practically set to a small number that is far less than $M$ .

As what will be discussed later, both the number and the quality of negatives affect the final performance of passage retrieval.

3.2.2 Inference

In our implementation, we use FAISS to index the dense representations of all passages.
使用了FAISS（Facebook AI Similarity Search）库来对所有段落的密集表示进行索引。

Specifically, we use IndexFlatIP for indexing and the exact maximuminner product search for querying.
具体地说，使用了 IndexFlatIP 作为索引类型，以及精确的最大内积搜索（exact maximum inner product search）作为查询方法。

FAISS：是一个高效相似性搜索和稠密向量聚类的库，尤其适用于在大型数据集上进行快速相似性搜索。
IndexFlatIP：这是一个基于平坦（flat）索引的FAISS类；
它直接存储了所有向量，并在查询时计算查询向量与所有存储向量的内积。
IP代表内积（Inner Product），所以 IndexFlatIP 适用于那些需要基于内积相似性度量（如余弦相似度）的应用场景。
最大内积搜索：这是基于内积相似度的一种搜索方法。对于给定的查询向量，它会找到与查询向量内积最大的存储向量。这在信息检索、推荐系统等领域中特别有用，因为这些领域通常涉及到计算向量之间的相似性。

通过结合使用IndexFlatIP和最大内积搜索，能够在大型文本集合中高效地找到与给定查询最相似的段落。

对于更大规模的数据集，可能需要考虑使用FAISS提供的更高效的索引方法，如基于聚类的索引（如IndexIVFPQ）或基于图的索引（如IndexHNSW），以在保持较高搜索质量的同时提高搜索速度。

不理解，没用过FAISS

3.3 Optimized Training Approach

Three major challenges in training the dual-encoder based retriever, including:

the training and inference discrepancy,
the existence of unlabeled positives,
limited training data.

3.3.1 Cross-batch Negatives

Assume that there are B questions in a mini-batch on a single GPU, and each question has one positive passage.
在这里插入图片描述
Figure 2: The comparison of traditional in-batch negatives and our cross-batch negatives when trained on multiple GPUs, where A is the number of GPUs, and B is the number of questions in each min-batch.

With A GPUs (or mini-batches) , we can indeed obtain $A \times B - 1$ negatives for a given question, which is approximately $A$ times as many as the original number of in-batch negatives.

In this way, we can use more negatives in the training objective of Equation 2, so that the results are expected to be improved.

3.3.2 Denoised Hard Negatives

因为人工标记的标签是有限的，存在大量未标记的正确答案；所以之前：

To obtain hard negatives, a straightforward method is to select the top-ranked passages (excluding the labeled positive passages) as negative samples.

这种方法，容易假阴；

基于此：

We first train a cross-encoder.

Then, when sampling hard negatives from the top-ranked passages retrieved by a dense retriever, we select only the passages that are predicted as negatives by the cross-encoder with high confidence scores.

The selected top-retrieved passages can be considered as denosied samples that are more reliable to be used as hard negatives.

3.3.3 Data Augmentation

The third strategy aims to alleviate the issue of limited training data.

Since the cross-encoder is more powerful in measuring the similarity between questions and passages, we utilize it to annotate unlabeled questions for data augmentation.

Specifically, we incorporate a new collection of unlabeled questions, while reuse the passage collection.

Then, we use the learned cross-encoder to predict the passage labels for the new questions.

To ensure the quality of the automatically labeled data, we only select the predicted positive and negative passages with high confidence scores estimated by the cross-encoder.

Finally, the automatically labeled data is used as augmented training data to learn the dual encoder.

3.4 The Training Procedure

在这里插入图片描述
Require:
Let $C$ denote a collection of passages.
$Q_{L}$ is a set of questions that have corresponding labeled passages in $C$ ,
$Q_{U}$ is a set of questions that have no corresponding labeled passages.
$D_{L}$ is a dataset consisting of $C$ and $Q_{L}$ ,
$D_{U}$ is a dataset consisting of $C$ and $Q_{U}$ .

Step1:
Train a dual-encoder $M_{D}^{(0)}$ by using cross-batch negatives on $D_{L}$ .

STEP 2:
Train a cross-encoder $M_{C}$ on $D_{L}$ .

The positives used for training the cross-encoder are from the original training set $D_{L}$ ,
while the negatives are randomly sampled from the top-k passages (excluding the labeled positive passages) retrieved by $M_{D}^{(0)}$ from $C$ for each question $\in D_{L}$ .

This design is to let the cross-encoder adjust to the distribution of the results retrieved by the dual-encoder, since the cross-encoder will be used in the following two steps for optimizing the dual-encoder.

STEP 3:
Train a dual-encoder $M_{D}^{(1)}$ by further introducing denoised hard negative sampling on $D_{L}$ .

Regarding to each question $\in D_{L}$ , the hard negatives are sampled from the top passages retrieved by $M_{D}^{(0)}$ from $C$ ,

and only the passages that are predicted as negatives by the cross-encoder $M_{C}$ with high confidence scores will be selected.

STEP 4:
Construct pseudo training data $D_{U}$ by using $M_{C}$ to label the top-k passages retrieved by $M_{D}^{(1)}$ from $C$ for each question $\in D_{U}$ ,

and then train a dual-encoder $M_{D}^{(2)}$ on both the manually labeled training data $D_{L}$ and the automatically augmented training data $D_{U}$ .

我个人理解为，

先用人工标记的数据集， $D_{L}$ ，训练一个检索模型 dual-encoder， $M_{D}^{(0)}$ ；

然后，训练一个分类模型，cross-encoder， $M_{C}$ ，该模型最后给出正负样本的二分类。其中，正样本来自 $D_{L}$ ，负样本来自： $M_{D}^{(0)}$ 给出的 top-k passages (excluding the labeled positive passages)。

然后，训练检索模型 dual-encoder， $M_{D}^{(1)}$ ；其增加的负样本，仍然来自 $M_{D}^{(0)}$ 给出的 top-k passages (excluding the labeled positive passages)，不过经过了一些筛选，是第二步中经过cross-encoder预测过为负样本的负样本；

这样会排除一些直接使用 $M_{D}^{(0)}$ 给出的 top-k passages (excluding the labeled positive passages)导致的未标记的正样本；

再然后，将 $D_{U}$ 喂给 $M_{D}^{(1)}$ ，get the top-k passages；将这些数据再喂给 $M_{C}$ 输出标签；

然后使用人工标记的 $D_{L}$ ，和，得到“伪标签”的 $D_{U}$ ，再训练一个检索模型 dual-encoder， $M_{D}^{(2)}$ 。

说 $M_{C}$ 是二分类模型是不合适的，结合 4.1.3 来看，其也是个检索模型：

4.1 Experimental Setup

4.1.3 Implementation Details

1. Pre-trained LMs

The dual-encoder is initialized with the parameters of ERNIE 2.0 base, and the cross-encoder is initialized with ERNIE 2.0 large.

2. Denoised hard negatives and data augmentation

We use the cross-encoder for both denoising hard negatives and data augmentation.

Specifically, we select the top retrieved passages with scores less than 0.1 as negatives and those with scores higher than 0.9 as positives.

We manually evaluated the selected data, and the accuracy was higher than 90%.

3. The number of positives and negatives

When training the cross-encoders, the ratios of the number of positives to the number of negatives are 1:4 and 1:1 on MSMARCO and NQ, respectively.

The negatives used for training cross-encoders are randomly sampled from the top-1000 and top-100 passages retrieved by the dual-encoder $M_{D}^{(0)}$ on MSMARCO and NQ, respectively.

When training the dual-encoders in the last two steps ( $M_{D}^{(1)}$ and $M_{D}^{(2)}$ ), we set the ratios of the number of positives to the number of hard negatives as 1:4 and 1:1 on MSMARCO and NQ, respectively.