With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few training examples. It has been a new trend exploring ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress, challenges, and future work in ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques of ICL, including training strategies, prompting strategies, and so on. Finally, we present the challenges of ICL and provide potential directions for further research. We hope our work can encourage more research on uncovering how ICL works and improving ICL in future work.

Large pretrained language models have shown surprising In-Context Learning (ICL) ability. With a few demonstration input-label pairs, they can predict the label for an unseen input without additional parameter updates. Despite the great success in performance, the working mechanism of ICL still remains an open problem. In order to better understand how ICL works, this paper explains language models as meta-optimizers and understands ICL as a kind of implicit finetuning. Theoretically, we figure out that the Transformer attention has a dual form of gradient descent based optimization. On top of it, we understand ICL as follows: GPT first produces meta-gradients according to the demonstration examples, and then these meta-gradients are applied to the original GPT to build an ICL model. Experimentally, we comprehensively compare the behavior of ICL and explicit finetuning based on real tasks to provide empirical evidence that supports our understanding. The results prove that ICL behaves similarly to explicit finetuning at the prediction level, the representation level, and the attention behavior level. Further, inspired by our understanding of meta-optimization, we design a momentum-based attention by analogy with the momentum-based gradient descent algorithm. Its consistently better performance over vanilla attention supports our understanding again from another aspect, and more importantly, it shows the potential to utilize our understanding for future model designing.

Datasets serve as crucial training resources and model performance trackers. However, existing datasets have exposed a plethora of problems, inducing biased models and unreliable evaluation results. In this paper, we propose a model-agnostic dataset evaluation framework for automatic dataset quality evaluation. We seek the statistical properties of the datasets and address three fundamental dimensions: reliability, difficulty, and validity, following a classical testing theory. Taking the Named Entity Recognition (NER) datasets as a case study, we introduce $9$ statistical metrics for a statistical dataset evaluation framework. Experimental results and human evaluation validate that our evaluation framework effectively assesses various aspects of the dataset quality. Furthermore, we study how the dataset scores on our statistical metrics affect the model performance, and appeal for dataset quality evaluation or targeted dataset improvement before training or testing models.

Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios. Prior work attempts to obtain 1-to-1 QA pairs from growing customer service chatlog, which fails to integrate the incomplete utterances from the dialog context for composite QA retrieval. In this paper, we propose N-to-N QA extraction task in which the derived questions and corresponding answers might be separated across different utterances. We introduce a suite of generative/discriminative tagging based methods with end-to-end and two-stage variants that perform well on 5 customer service datasets and for the first time setup a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics. With a deep dive into extracted QA pairs, we find that the relations between and inside the QA pairs can be indicators to analyze the dialogue structure, e.g. information seeking, clarification, barge-in and elaboration. We also show that the proposed models can adapt to different domains and languages, and reduce the labor cost of knowledge accumulation in the real-world product dialogue platform.

连续的关系提取（CRE）要求该模型不断从课堂收入数据流中学习新关系。在本文中，我们提出了一种令人沮丧的简单但有效的方法（FEA）方法，其中有两个学习阶段的CRE：1）快速适应（FA）仅使用新数据加热模型。 2）平衡调整（BT）列出平衡内存数据上的模型。尽管它很简单，但FEA与最先进的基线相比，FEA取得了可比性（在诱人或优越（在少数情况下）性能。通过仔细的检查，我们发现新关系之间的数据失衡会导致偏斜的决策边界在预计编码器上的头部分类器中，从而损害了整体性能。在FEA中，FA阶段释放了后续填充的内存数据的潜力，而BT阶段有助于建立更平衡的决策边界。通过统一的视图，我们，我们发现可以将两个强大的CRE基准列入提议的培训管道中。FEEA的成功还为CRE中的未来模型设计提供了可行的见解和建议。

预处理的变形金刚记住事实知识的能力对于下游任务（例如封闭式问题答案）是必不可少的。现有的工作表明，经过审计的变压器可以回忆或利用在某种程度上出现的训练训练阶段中出现的事实知识。但是，由于模型能力的限制，预审预周仔的记忆知识的能力也受到限制。 Dai等。 （2022）发现经过验证的变形金刚中的馈电网络（FFN）以内存的方式存储事实知识。受这一发现的启发，我们提出了一个神经知识库（NKB），以存储预验证的变压器的额外事实知识。要具体而言，我们还将FFN视为键值记忆，并使用其他内存插槽扩展它们。在知识注入期间，我们将原始模型和事实知识注入扩展的存储插槽中，因此预验证的模型不会遗忘。此外，FFN作为钥匙值记忆的观点使NKB高度可解释。我们使用三个封闭式问题回答数据集来显示我们强大的存储额外事实知识的能力。另外，我们证明NKB不会通过两种代表性生成任务，摘要和机器翻译来降低验证模型的一般语言生成能力。此外，我们彻底分析了NKB以揭示其工作机制，并以人为可读的方式介绍其钥匙和价值观的含义。最重要的是，我们执行初步尝试，以直接更新NKB中的事实知识，而无需任何其他培训。

实现通用语言情报是自然语言处理的长期目标，标准评估基准发挥基本和指导作用。我们认为，对于通用语言智能评估，基准本身需要全面和系统。为此，我们提出了Cuge，一种中文语言理解和生成评估基准，具有以下特征：（1）分层基准框架，其中数据集主要选择和组织语言能力 - 任务数据集层次结构。 （2）多级评分策略，其中基于分层框架提供了不同级别的模型性能。为了促进CUGE，我们提供了一个公共排行榜，可以自定义，以支持灵活的模型判断标准。代表性预先训练的语言模型的评估结果表明了对通用语言智能的完善的充足空间。 Cuge在Cuge.baai.ac.cn上公开提供。

方面情绪三重态提取（Aste）旨在识别目标，他们的情感极化和意见解释句子的情绪。 Aste可以自然地分为3个原子子组织，即目标检测，意见检测和情绪分类。我们认为针对目标 - 意见对的合适的子任务组合，组成特征提取，以及子任务之间的互动将是成功的关键。然而，由于缺陷的子任务制定，子最优特征表示或缺少子任务相互作用，在“一对多”或“多对一”的情况下可能导致不存在的情绪三体，或导出不存在的情绪三元组。在本文中，我们将Aste划分为目标 - 意见联合检测和情绪分类子任务，这与人类认知符合，并且相应地利用序列编码器和表编码器来处理它们。表编码器在令牌对等级提取情绪，从而可以容易地捕获目标和意见之间的组成特征。要在子任务之间建立显式交互，我们利用表格表示来指导序列编码，并将序列功能注入到表编码器中。实验表明，我们的模型在六个受欢迎的ASTE数据集中优于最先进的方法。

Reinforcement learning (RL) is one of the most important branches of AI. Due to its capacity for self-adaption and decision-making in dynamic environments, reinforcement learning has been widely applied in multiple areas, such as healthcare, data markets, autonomous driving, and robotics. However, some of these applications and systems have been shown to be vulnerable to security or privacy attacks, resulting in unreliable or unstable services. A large number of studies have focused on these security and privacy problems in reinforcement learning. However, few surveys have provided a systematic review and comparison of existing problems and state-of-the-art solutions to keep up with the pace of emerging threats. Accordingly, we herein present such a comprehensive review to explain and summarize the challenges associated with security and privacy in reinforcement learning from a new perspective, namely that of the Markov Decision Process (MDP). In this survey, we first introduce the key concepts related to this area. Next, we cover the security and privacy issues linked to the state, action, environment, and reward function of the MDP process, respectively. We further highlight the special characteristics of security and privacy methodologies related to reinforcement learning. Finally, we discuss the possible future research directions within this area.

In this paper, we introduce a novel approach for ground plane normal estimation of wheeled vehicles. In practice, the ground plane is dynamically changed due to braking and unstable road surface. As a result, the vehicle pose, especially the pitch angle, is oscillating from subtle to obvious. Thus, estimating ground plane normal is meaningful since it can be encoded to improve the robustness of various autonomous driving tasks (e.g., 3D object detection, road surface reconstruction, and trajectory planning). Our proposed method only uses odometry as input and estimates accurate ground plane normal vectors in real time. Particularly, it fully utilizes the underlying connection between the ego pose odometry (ego-motion) and its nearby ground plane. Built on that, an Invariant Extended Kalman Filter (IEKF) is designed to estimate the normal vector in the sensor's coordinate. Thus, our proposed method is simple yet efficient and supports both camera- and inertial-based odometry algorithms. Its usability and the marked improvement of robustness are validated through multiple experiments on public datasets. For instance, we achieve state-of-the-art accuracy on KITTI dataset with the estimated vector error of 0.39{\deg}. Our code is available at github.com/manymuch/ground_normal_filter.