对于开放式对话问题应答（CQA），可以检索最相关的段落来回答问题，但与标准通道检索相比，这是具有挑战性，因为它需要了解完整的对话背景而不是单个查询。此外，重新列车良好的检索者（例如用于非对话查询）最初开发的搜索引擎都可以昂贵。为了便于他们的使用，我们开发了一个查询重写模型Conqrr，可以将上下文中的会话问题重写为独立的问题。它培训了一种新颖的奖励功能，可以直接优化检索，并且可以使用强化学习来适应任何固定的黑箱猎犬。我们展示了Conqrr在最近的开放式CQA数据集上实现了最先进的结果，这是来自三个不同来源的对话组合。我们还开展了广泛的实验，以表明CONQRR为任何给定的固定猎犬的有效性。

在寻求信息的对话中，用户与代理商进行对话，以提出一系列通常可以不足或过度指定的问题。理想的代理商首先将通过搜索其基本知识来源，然后与用户进行适当互动以解决它，从而确定他们处于这种情况。但是，大多数现有研究都无法或人为地纳入此类代理端计划。在这项工作中，我们介绍了Inscit（发音为Insight），这是一种用于与混合互动相互作用的信息寻求对话的数据集。它包含从805个人类对话中进行的4.7k用户代理转弯，代理商对Wikipedia进行搜索，并要求澄清或提供相关信息以解决用户查询。我们定义了两个子任务，即证据通过识别和响应产生，以及一种新的人类评估协议来评估模型绩效。我们根据对话知识识别和开放域问题的最新模型报告了两个强大的基线的结果。这两种模型都显着不足，并且没有产生连贯和信息丰富的反应，这表明未来的研究有足够的改进空间。

We present Hybrid Infused Reranking for Passages Retrieval (HYRR), a framework for training rerankers based on a hybrid of BM25 and neural retrieval models. Retrievers based on hybrid models have been shown to outperform both BM25 and neural models alone. Our approach exploits this improved performance when training a reranker, leading to a robust reranking model. The reranker, a cross-attention neural model, is shown to be robust to different first-stage retrieval systems, achieving better performance than rerankers simply trained upon the first-stage retrievers in the multi-stage systems. We present evaluations on a supervised passage retrieval task using MS MARCO and zero-shot retrieval tasks using BEIR. The empirical results show strong performance on both evaluations.

检索增强的代表在许多知识密集型的NLP任务中表现出最先进的表现，例如打开问题应答和事实验证。考虑到检索到的段落，这些模型训练以产生最终输出，这可能与原始查询无关，导致学习虚假线索或回答记忆。这项工作介绍了一种融入通道的证据性的方法 - 是否段落包含正确的证据来支持输出 - 培训发电机。我们介绍了一个多任务学习框架，共同生成最终输出并预测每个段落的证据性，利用新的任务不可行方法来获得{\ IT Silver}分证分性标签进行监督。我们在三个知识密集型任务中的五个数据集的实验表明，我们的新的证据引导发电机具有相同尺寸模型的直接对应的直接对应，并使Faviq-Ambig的最先进。我们将这些改进归因于辅助多任务学习和银证处分性挖掘技术。

Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dualencoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system greatly by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks. 1 * Equal contribution 1 The code and trained models have been released at https://github.com/facebookresearch/DPR.

The quality of knowledge retrieval is crucial in knowledge-intensive conversations. Two common strategies to improve the retrieval quality are finetuning the retriever or generating a self-contained query, while they encounter heavy burdens on expensive computation and elaborate annotations. In this paper, we propose an unsupervised query enhanced approach for knowledge-intensive conversations, namely QKConv. There are three modules in QKConv: a query generator, an off-the-shelf knowledge selector, and a response generator. Without extra supervision, the end-to-end joint training of QKConv explores multiple candidate queries and utilizes corresponding selected knowledge to yield the target response. To evaluate the effectiveness of the proposed method, we conducted comprehensive experiments on conversational question-answering, task-oriented dialogue, and knowledge-grounded conversation. Experimental results demonstrate that QKConv achieves state-of-the-art performance compared to unsupervised methods and competitive performance compared to supervised methods.

关于信息检索的许多最新研究集中在如何从一项任务（通常具有丰富的监督数据）转移到有限的其他各种任务，并隐含地假设可以从一个任务概括到所有其余的任务。但是，这忽略了这样一个事实，即有许多多样化和独特的检索任务，每个任务都针对不同的搜索意图，查询和搜索域。在本文中，我们建议使用几乎没有散热的检索，每个任务都有一个简短的描述和一些示例。为了扩大一些示例的功能，我们提出了针对检索器（即将到来）的及时基本查询生成，该查询将大型语言模型（LLM）作为几个弹片查询生成器，并根据生成的数据创建特定于任务的检索器。通过LLM的概括能力提供动力，即要来源使得可以仅基于一些示例{没有自然问题或MS MARCO来训练％问题生成器或双重编码器，就可以仅基于一些示例{没有}来创建特定于任务的端到端检索。出乎意料的是，LLM提示不超过8个示例，允许双重编码器在MARCO（例如Colbert V2）上训练的大量工程模型平均在11个检索套件中超过1.2 NDCG。使用相同生成数据的进一步培训标准尺寸的重新级别可获得5.0点NDCG的改进。我们的研究确定，查询产生比以前观察到的更有效，尤其是在给出少量特定于任务知识的情况下。

我们介绍了Art，这是一种新的语料库级自动编码方法，用于培训密集检索模型，不需要任何标记的培训数据。密集的检索是开放域任务（例如Open QA）的核心挑战，在该任务中，最先进的方法通常需要大量的监督数据集，并具有自定义的硬性采矿和肯定式示例。相反，艺术品仅需要访问未配对的投入和输出（例如问题和潜在的答案文件）。它使用新的文档 - 重新定义自动编码方案，其中（1）输入问题用于检索一组证据文档，并且（2）随后使用文档来计算重建原始问题的概率。基于问题重建的检索培训可以有效地学习文档和问题编码器，以后可以将其纳入完整的QA系统中，而无需任何进一步的填充。广泛的实验表明，ART在多个QA检索基准测试基准上获得最先进的结果，并且仅来自预训练的语言模型的一般初始化，从而消除了对标记的数据和特定于任务的损失的需求。

Large language models (LLMs) have shown impressive results across a variety of tasks while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios. We believe the ability of an LLM to attribute the text that it generates is likely to be crucial for both system developers and users in this setting. We propose and study Attributed QA as a key first step in the development of attributed LLMs. We develop a reproducable evaluation framework for the task, using human annotations as a gold standard and a correlated automatic metric that we show is suitable for development settings. We describe and benchmark a broad set of architectures for the task. Our contributions give some concrete answers to two key questions (How to measure attribution?, and How well do current state-of-the-art methods perform on attribution?), and give some hints as to how to address a third key question (How to build LLMs with attribution?).

多跳的推理（即跨两个或多个文档的推理）是NLP模型的关键要素，该模型利用大型语料库表现出广泛的知识。为了检索证据段落，多跳模型必须与整个啤酒花的快速增长的搜索空间抗衡，代表结合多个信息需求的复杂查询，并解决有关在训练段落之间跳出的最佳顺序的歧义。我们通过Baleen解决了这些问题，Baleen可以提高多跳检索的准确性，同时从多跳的训练信号中学习强大的训练信号的准确性。为了驯服搜索空间，我们提出了凝结的检索，该管道总结了每个跃点后检索到单个紧凑型上下文的管道。为了建模复杂的查询，我们引入了一个重点的后期相互作用检索器，该检索器允许同一查询表示的不同部分匹配不同的相关段落。最后，为了推断无序的训练段落中的跳跃依赖性，我们设计了潜在的跳跃订购，这是一种弱者的策略，在该策略中，受过训练的检索员本身选择了啤酒花的顺序。我们在检索中评估Baleen的两跳问答和多跳的要求验证，并确定最先进的绩效。

Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge remain open research problems. Pre-trained models with a differentiable access mechanism to explicit nonparametric memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) -models which combine pre-trained parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages across the whole generated sequence, and another which can use different passages per token. We fine-tune and evaluate our models on a wide range of knowledge-intensive NLP tasks and set the state of the art on three open domain QA tasks, outperforming parametric seq2seq models and task-specific retrieve-and-extract architectures. For language generation tasks, we find that RAG models generate more specific, diverse and factual language than a state-of-the-art parametric-only seq2seq baseline.

已经表明，在一个域上训练的双编码器经常概括到其他域以获取检索任务。一种广泛的信念是，一个双编码器的瓶颈层，其中最终得分仅仅是查询向量和通道向量之间的点产品，它过于局限，使得双编码器是用于域外概括的有效检索模型。在本文中，我们通过缩放双编码器模型的大小{\ em同时保持固定的瓶颈嵌入尺寸固定的瓶颈的大小来挑战这一信念。令人惊讶的是，令人惊讶的是，缩放模型尺寸会对各种缩放提高检索任务，特别是对于域外泛化。实验结果表明，我们的双编码器，\ textbf {g} enovalizable \ textbf {t} eTrievers（gtr），优先级％colbert〜\ cite {khattab2020colbertt}和现有的稀疏和密集的索取Beir DataSet〜\ Cite {Thakur2021Beir}显着显着。最令人惊讶的是，我们的消融研究发现，GTR是非常数据的高效，因为它只需要10 \％MARCO监督数据，以实现最佳域的性能。所有GTR模型都在https://tfhub.dev/google/collections/gtr/1发布。

大型语言模型可以产生流畅的对话，但往往是幻觉的事实不准确。虽然检索式增强的模型有助于缓解这个问题，但他们仍然面临着推理的艰难挑战，以便同时提供正确的知识和产生对话。在这项工作中，我们提出了一种模块化模型，知识响应（K2R），将知识纳入会话代理商，这将这个问题分解为两个更简单的步骤。 K2R首先生成一个知识序列，给定对话背景作为中间步骤。在此“推理步骤”之后，该模型随后参加自己生成的知识序列，以及对话背景，以产生最终的响应。在详细的实验中，我们发现这种模型在知识接地的对话任务中少幻觉，并且在可解释性和模块化方面具有优势。特别地，它可以用来将QA和对话系统一起融合在一起，以使对话代理能够提供知识渊博的答案，或者QA模型，以在零拍摄设置中给出对话响应。

以查询为中心的摘要（QFS）旨在产生应答感兴趣的特定问题的摘要，从而实现更大的用户控制和个性化。虽然最近发布的数据集如QMSUM或Aquamuse，促进QFS中的研究工作，但该领域缺乏对适用建模方法的广泛空间的全面研究。在本文中，考虑到两种普遍的方法，我们对QFS进行了系统探索，探讨了QFS：两阶段的采掘解决方案和端到端模型。在这些类别中，我们调查现有方法，并呈现了在QMSUM数据集上实现最先进的性能的两个模型扩展，其边缘高达3.38 Rouge-1，3.72 Rouge-2和3.28 Rouge-L。通过定量实验，我们突出了不同模型配置之间的权衡，并探讨了摘要任务之间的转移能力。代码和检查点公开可用：https：//github.com/salesforce/query-focused-sum。

Recent developments of dense retrieval rely on quality representations of queries and contexts coming from pre-trained query and context encoders. In this paper, we introduce TouR (test-time optimization of query representations), which further optimizes instance-level query representations guided by signals from test-time retrieval results. We leverage a cross-encoder re-ranker to provide fine-grained pseudo labels over retrieval results and iteratively optimize query representations with the gradient descent method. Our theoretical analysis reveals that TouR can be viewed as a generalization of the classical Rocchio's algorithm for pseudo relevance feedback, and we present two variants leveraging psuedo labels as either hard binary or soft continuous labels. We first apply TouR on phrase retrieval with our proposed phrase re-ranker. On passage retrieval, we demonstrate its effectiveness with an off-the-shelf re-ranker. TouR improves the end-to-end open-domain QA accuracy significantly, as well as passage retrieval performance. Compared to re-ranker, TouR requires a smaller number of candidates, and achieves consistently better performance and runs up to 4x faster with our efficient implementation.

We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries. We aim to develop a general-purpose task-aware retrieval system using multi-task instruction tuning, which can follow human-written instructions to find the best documents for a given query. We introduce the first large-scale collection of approximately 40 retrieval datasets with instructions, BERRI, and present TART, a multi-task retrieval system trained on BERRI with instructions. TART shows strong capabilities to adapt to a new retrieval task via instructions and advances the state of the art on two zero-shot retrieval benchmarks, BEIR and LOTTE, outperforming models up to three times larger. We further introduce a new evaluation setup, X^2-Retrieval to better reflect real-world scenarios, where diverse domains and tasks are pooled and a system needs to find documents aligning users' intents. In this setup, TART significantly outperforms competitive baselines, further demonstrating the effectiveness of guiding retrieval with instructions.

大型语言模型在各种任务上显示出令人印象深刻的几次结果。但是，当知识是此类结果的关键时，就像问题回答和事实检查之类的任务一样，似乎需要存储知识的大量参数计数。众所周知，检索增强模型可以在不需要多个参数的情况下在知识密集的任务上表现出色，但是目前尚不清楚它们是否在几个弹药设置中工作。在这项工作中，我们介绍了地图集，这是一个经过精心设计和预先训练的增强语言模型，能够通过很少的培训示例学习知识密集型任务。我们对包括MMLU，苏格兰短裙和归类等各种任务进行评估，并研究文档索引内容的影响，表明它可以很容易地进行更新。值得注意的是，在自然问题上仅使用64个示例在自然问题上达到超过42 \％的准确性，尽管参数少了50倍，但比540B参数模型的表现优于540b参数模型。

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

我们处理与混合倡议的会话搜索方案：即用户询问系统答案，以及系统询问（澄清问题）和用户答案。我们专注于选择下一个澄清问题的任务，给定对话上下文。我们的方法利用通道检索，用于初始选择相关候选澄清问题，以及微调两个深度学习模型，用于重新排名这些候选人。我们在两种不同用例中评估了我们的方法。第一个是在大型Web集合中的开放式域会话搜索。第二个是面向任务的客户支持设置。我们展示我们的方法在两个使用情况下表现良好。

本文介绍了学习迭代查询细化的元策略的设计代理的首先成功步骤。我们的方法使用机器读取来指导从聚合搜索结果中选择细化项。然后，使用简单但有效的搜索操作员能够赋予代理，以对查询和搜索结果发挥细粒度和透明控制。我们开发一种新颖的方式来发电综合搜索会话，它通过（自我）监督学习来利用基于变压器的语言模型的力量。我们还提出了一种强化学习代理，具有动态约束的动作，从划痕中了解互动搜索策略。我们使用传统的基于术语的BM25排名函数获得与最近神经方法相当的检索和回答质量性能。我们对搜索政策进行了深入的分析。