State-of-the-art language models are often accurate on many question-answering benchmarks with well-defined questions. Yet, in real settings questions are often unanswerable without asking the user for clarifying information. We show that current SotA models often do not ask the user for clarification when presented with imprecise questions and instead provide incorrect answers or "hallucinate". To address this, we introduce CLAM, a framework that first uses the model to detect ambiguous questions, and if an ambiguous question is detected, prompts the model to ask the user for clarification. Furthermore, we show how to construct a scalable and cost-effective automatic evaluation protocol using an oracle language model with privileged information to provide clarifying information. We show that our method achieves a 20.15 percentage point accuracy improvement over SotA on a novel ambiguous question-answering answering data set derived from TriviaQA.

Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM). Existing work has combined these in simple "retrieve-then-read" pipelines in which the RM retrieves passages that are inserted into the LM prompt. To begin to fully realize the potential of frozen LMs and RMs, we propose Demonstrate-Search-Predict (DSP), a framework that relies on passing natural language texts in sophisticated pipelines between an LM and an RM. DSP can express high-level programs that bootstrap pipeline-aware demonstrations, search for relevant passages, and generate grounded predictions, systematically breaking down problems into small transformations that the LM and RM can handle more reliably. We have written novel DSP programs for answering questions in open-domain, multi-hop, and conversational settings, establishing in early evaluations new state-of-the-art in-context learning results and delivering 37-200%, 8-40%, and 80-290% relative gains against vanilla LMs, a standard retrieve-then-read pipeline, and a contemporaneous self-ask pipeline, respectively.

我们介绍了Sparrow，这是一个寻求信息的对话代理，与提示的语言模型基线相比，训练有素，更有帮助，正确和无害。我们使用从人类反馈中的强化学习来培训我们的模型，以帮助人类评估者判断代理人的行为。首先，为了使我们的代理人更有帮助和无害，我们将良好对话的要求分解为代理人应遵循的自然语言规则，并分别向评估者询问每个规则。我们证明，这种崩溃使我们能够收集对代理行为的更多针对性的人类判断，并允许更有效的规则条件奖励模型。其次，我们的代理商在收集对模型声明的偏好判决时提供了支持事实主张的来源的证据。对于事实问题，麻雀提供的证据支持了78％的时间。比基线比基线更享受麻雀，同时对人类的对抗性探测更具弹性，在探测时只有8％的时间违反了我们的规则。最后，我们进行了广泛的分析，表明尽管我们的模型学会遵守我们的规则，但它可以表现出分布偏见。

Many real-world applications of language models (LMs), such as code autocomplete and writing assistance, involve human-LM interaction, but the main LM benchmarks are non-interactive, where a system produces output without human intervention. To evaluate human-LM interaction, we develop a framework, Human-AI Language-based Interaction Evaluation (H-LINE), that expands non-interactive evaluation along three dimensions, capturing (i) the interactive process, not only the final output; (ii) the first-person subjective experience, not just a third-party assessment; and (iii) notions of preference beyond quality. We then design five tasks ranging from goal-oriented to open-ended to capture different forms of interaction. On four state-of-the-art LMs (three variants of OpenAI's GPT-3 and AI21's J1-Jumbo), we find that non-interactive performance does not always result in better human-LM interaction and that first-person and third-party metrics can diverge, suggesting the importance of examining the nuances of human-LM interaction.

最近已被证明大型语言模型在各种任务集中获得合理的零射普通化（Brown等，2020）。它已经假设这是语言模型的隐式多任务学习的结果，在语言模型中的预押（Radford等，2019）。可以通过明确的多任务学习直接引起零拍常规化？为了以缩放测试这个问题，我们开发一个系统，以便轻松地将任何自然语言任务映射到人类可读的提示表单中。我们转换一组大量的监督数据集，每个数据集都有多个提示，具有不同的措辞。这些提示的数据集允许基准测试模型执行完全看不见的任务的能力。我们介绍了一个普拉克尔编码器 - 解码器模型（Raffel等，2020; Lester等，2021），覆盖各种任务。该模型在多个标准数据集中达到强大的零点性能，通常优于其尺寸的型号超过16倍。此外，我们的方法对来自Big-替补基准测试的任务子集具有强烈性能，优于其尺寸的6倍。所有提示和培训的型号都可以在https://github.com/ bigscience-workshop / protectsource / httpsource / https：//huggingface.co/bigscience/t0pp。

我们提出了Tacobot，这是为首届Alexa Prive Taskbot Challenge构建的面向任务的对话系统，该系统可帮助用户完成多步骤烹饪和家庭装修任务。Tacobot的设计采用以用户为中心的原则，并渴望提供协作且易于访问的对话体验。为此，它具有准确的语言理解，灵活的对话管理和引人入胜的响应生成。此外，Tacobot还以强大的搜索引擎和自动化的端到端测试套件为支持。在引导Tacobot的开发中，我们探索了一系列数据增强策略，以训练先进的神经语言处理模型，并通过收集的真实对话不断改善对话经验。在半决赛结束时，Tacobot的平均评分为3.55/5.0。

问答系统被认为是流行且经常有效的信息在网络上寻求信息的手段。在这样的系统中，寻求信息者可以通过自然语言提出问题来获得对他们的查询的简短回应。交互式问题回答是一种最近提出且日益流行的解决方案，它位于问答和对话系统的交集。一方面，用户可以以普通语言提出问题，并找到对她的询问的实际回答；另一方面，如果在初始请求中有多个可能的答复，很少或歧义，则系统可以将问题交通会话延长到对话中。通过允许用户提出更多问题，交互式问题回答使用户能够与系统动态互动并获得更精确的结果。这项调查提供了有关当前文献中普遍存在的交互式提问方法的详细概述。它首先要解释提问系统的基本原理，从而定义新的符号和分类法，以将所有已确定的作品结合在统一框架内。然后，根据提出的方法，评估方法和数据集/应用程序域来介绍和检查有关交互式问题解答系统的审查已发表的工作。我们还描述了围绕社区提出的特定任务和问题的趋势，从而阐明了学者的未来利益。 GitHub页面的综合综合了本文献研究中涵盖的所有主要主题，我们的工作得到了进一步的支持。 https://sisinflab.github.io/interactive-question-answering-systems-survey/

尽管改善神经对话代理的事实准确性是大量研究的对象，但在神经对话的环境中，沟通的另一个重要方面是对无知的透明度。在这项工作中，我们分析了最新的聊天模型在多大程度上是语言校准的，因为它们的疑问（或信心）的口头表达与该模型的响应实际上是不正确（或正确）的可能性相匹配。 。我们发现这些模型的校准很差，但是我们表明可以准确预测正确性的可能性。通过将这种元认知特征纳入可控生成模型的训练中，我们获得了具有大大改进语言校准的对话代理。尽管改善神经对话代理的事实准确性是大量研究的对象，但在神经对话的环境中，沟通的另一个重要方面是对无知的透明度。在这项工作中，我们分析了最新的聊天模型在多大程度上是语言校准的，因为它们的疑问（或信心）的口头表达与该模型的响应实际上是不正确（或正确）的可能性相匹配。 。我们发现这些模型的校准很差，但是我们表明可以准确预测正确性的可能性。通过将这种元认知特征纳入可控生成模型的训练中，我们获得了具有大大改进语言校准的对话代理。

大型预训练的语言模型已经表现出了产生现实文本的强大功能。但是，控制生成结果仍然具有挑战性。以前的方法，例如提示远远不足，这限制了语言模型的使用。为了解决这一挑战，我们提出了一种创新的方法，逆提示，更好地控制文本生成。逆提示的核心思想是使用生成的文本来在波束搜索期间反转提示，这增强了提示和生成文本之间的相关性，并提供了更好的可控性。经验上，我们预先培训了大规模的汉语模型，在开放式诗歌生成和开放式长形问题的任务上使用人力评估进行系统研究。我们的研究结果表明，我们的提出方法显着优于基线，而我们的发电质量与某些任务中的某些任务接近人类性能。叙述者可以在https://pretrain.aminer.cn/apps/poetry.html上尝试我们的诗歌生成演示，而我们的QA演示可以在https://pretrain.aminer.cn/app/qa找到。对于研究人员来说，代码是在https://github.com/thudm/inverseprompting中提供的。

知识密集型任务，例如开放域问题答案（QA），需要访问大量的世界知识或领域知识。知识密集型任务的一种常见方法是采用检索到阅读的管道，该管道首先从诸如Wikipedia之类的外部语料库中检索少数相关的上下文文档，然后预测在检索文档的条件下得到答案。在本文中，我们提出了一种新的观点，可以通过用大型语言模型生成器代替文档检索器来解决知识密集型任务。我们称我们的方法生成-Read Read（GenRead），该方法首先提示大型语言模型根据给定问题生成上下文文档，然后读取生成的文档以产生最终答案。此外，我们提出了一种基于聚类的提示方法，该方法选择了不同的提示，从而产生了涵盖不同观点的生成文档，从而更好地回忆了可接受的答案。我们对三个不同的知识密集任务进行了广泛的实验，包括开放域质量检查，事实检查和对话系统。值得注意的是，GenRead在Triviaqa和WebQ上实现了71.6和54.4的精确匹配分数，显着超过了最先进的检索到+4.0和+3.9的最先进的dpr-fid，而无需从任何外部知识源中检索任何文档。最后，我们证明可以通过结合检索和生成来进一步提高模型性能。

在本文中，我们介绍了基于大型预训练的语言模型（PLM）pangu-alpha（Zeng等，2021）的中国预训练的开放域对话生成模型。与其他对大量对话数据进行培训的预训练的对话模型不同，我们旨在通过继承PLM的有价值的语言能力和知识来构建强大的对话模型，并以相对较少的数据和计算成本构建强大的对话模型。为此，我们训练大型PLM Pangu-Alpha的Pangu-bot，该机器人已被证明在各种中国自然语言任务上表现出色。我们研究了pangu-bot产生的响应的不同方面，包括响应质量，知识和安全性。我们表明，Pangu-Bot优于最先进的中国对话系统（CDIALGPT（Wang等，2020），Eva（Zhou等，2021），EVA2.0（Gu等，2022）） W.R.T.以上三个方面。我们还证明，可以轻松地部署pangu-bot，以在没有进一步训练的情况下产生情感反应。在整个经验分析中，我们还指出，Pangu-bot响应质量，知识正确性和安全性仍然远非完美，进一步的探索对于建立可靠且智能的对话系统是必不可少的。我们的型号和代码将在https://github.com/huawei-noah/pretretaining-language-model/tree/master/master/pangu-bot上提供。

Naturally-occurring information-seeking questions often contain questionable assumptions -- assumptions that are false or unverifiable. Questions containing questionable assumptions are challenging because they require a distinct answer strategy that deviates from typical answers to information-seeking questions. For instance, the question "When did Marie Curie discover Uranium?" cannot be answered as a typical when question without addressing the false assumption "Marie Curie discovered Uranium". In this work, we propose (QA)$^2$ (Question Answering with Questionable Assumptions), an open-domain evaluation dataset consisting of naturally-occurring search engine queries that may or may not contain questionable assumptions. To be successful on (QA)$^2$, systems must be able to detect questionable assumptions and also be able to produce adequate responses for both typical information-seeking questions and ones with questionable assumptions. We find that current models do struggle with handling questionable assumptions -- the best performing model achieves 59% human rater acceptability on abstractive QA with (QA)$^2$ questions, leaving substantial headroom for progress.

Language models have recently achieved strong performance across a wide range of NLP benchmarks. However, unlike benchmarks, real world tasks are often poorly specified, and agents must deduce the user's intended behavior from a combination of context, instructions, and examples. We investigate how both humans and models behave in the face of such task ambiguity by proposing AmbiBench, a new benchmark of six ambiguously-specified classification tasks. We evaluate humans and models on AmbiBench by seeing how well they identify the intended task using 1) instructions with varying degrees of ambiguity, and 2) different numbers of labeled examples. We find that the combination of model scaling (to 175B parameters) and training with human feedback data enables models to approach or exceed the accuracy of human participants across tasks, but that either one alone is not sufficient. In addition, we show how to dramatically improve the accuracy of language models trained without large-scale human feedback training by finetuning on a small number of ambiguous in-context examples, providing a promising direction for teaching models to generalize well in the face of ambiguity.

This paper proposes a question-answering system that can answer questions whose supporting evidence is spread over multiple (potentially long) documents. The system, called Visconde, uses a three-step pipeline to perform the task: decompose, retrieve, and aggregate. The first step decomposes the question into simpler questions using a few-shot large language model (LLM). Then, a state-of-the-art search engine is used to retrieve candidate passages from a large collection for each decomposed question. In the final step, we use the LLM in a few-shot setting to aggregate the contents of the passages into the final answer. The system is evaluated on three datasets: IIRC, Qasper, and StrategyQA. Results suggest that current retrievers are the main bottleneck and that readers are already performing at the human level as long as relevant passages are provided. The system is also shown to be more effective when the model is induced to give explanations before answering a question. Code is available at \url{https://github.com/neuralmind-ai/visconde}.

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。

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.

Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on most skills relevant to the task at hand. Empirical work on this problem is not straightforward, since we do not yet have systems that broadly exceed our abilities. This paper discusses one of the major ways we think about this problem, with a focus on how to turn it into one that can be productively studied empirically. We first present an experimental design centered on choosing tasks for which human specialists succeed but unaided humans and current general AI systems fail. We then present a proof-of-concept experiment following meant to demonstrate a key feature of this experimental design and show its viability with two question-answering tasks: MMLU and time-limited QuALITY. On these tasks, we find that human participants who interact with an unreliable large-language-model dialog assistant through chat -- a trivial baseline strategy for scalable oversight -- substantially outperform both the model alone and their own unaided performance. These results are an encouraging sign that scalable oversight will be tractable to study with present models and bolster recent findings that large language models can productively assist humans with difficult tasks.

Instruction tuning enables pretrained language models to perform new tasks from inference-time natural language descriptions. These approaches rely on vast amounts of human supervision in the form of crowdsourced datasets or user interactions. In this work, we introduce Unnatural Instructions: a large dataset of creative and diverse instructions, collected with virtually no human labor. We collect 64,000 examples by prompting a language model with three seed examples of instructions and eliciting a fourth. This set is then expanded by prompting the model to rephrase each instruction, creating a total of approximately 240,000 examples of instructions, inputs, and outputs. Experiments show that despite containing a fair amount of noise, training on Unnatural Instructions rivals the effectiveness of training on open-source manually-curated datasets, surpassing the performance of models such as T0++ and Tk-Instruct across various benchmarks. These results demonstrate the potential of model-generated data as a cost-effective alternative to crowdsourcing for dataset expansion and diversification.

我们研究语言模型是否可以评估自己主张的有效性，并预测他们能够正确回答的问题。我们首先表明，当以正确的格式提供时，较大的模型在多样化的多项选择和True/False问题上进行了很好的校准。因此，我们可以通过要求模型首先提出答案，然后评估其答案正确的概率“ p（true）”来对开放式采样任务进行自我评估。我们发现在各种任务中，P（true）的表现，校准和缩放令人鼓舞。当我们允许模型考虑自己的许多样本之前，在预测一种特定可能性的有效性之前，自我评估的性能进一步改善。接下来，我们研究是否可以培训模型来预测“ P（ik）”，即“我知道”问题的概率，而无需参考任何特定提出的答案。模型在预测P（IK）方面表现良好，并且在跨任务中部分概括，尽管它们在新任务上的P（IK）校准方面遇到了困难。预测的p（IK）概率在存在相关的原始材料的情况下以及对数学单词问题解决方案的提示也适当增加。我们希望这些观察结果为培训更诚实的模型提供了基础，并研究了诚实对模型模仿人类写作以外的其他目标培训的案例的普遍性。

对话式AI中的现有研究主要将面向任务的对话框（TOD）和问题答案（QA）视为单独的任务。为了构建可以完成用户任务和支持信息寻求信息的对话代理的目标，构建一个可以访问各种外部知识的系统，构建一个处理TOD和QA的系统非常重要。在这项工作中，我们提出了一项新任务，开放式TOD（OB-TOD），将TOD与QA任务相结合，并将外部知识源扩展到包括明确的知识源（例如Web）和隐式知识源（例如，例如，预训练的语言模型）。我们创建了一个新的数据集ob-multiwoz，在这里，我们在其中丰富了Tod会议，并使用类似QA的信息寻求基于外部知识的经验。我们提出了一个统一的模型Opera（开放式末端到端任务对话框），可以适当地访问明确和隐性的外部知识，以解决定义的任务。实验结果表明，与闭环基线相比，Opera的表现出色，并说明了两种知识类型的价值。