Tool-augmented Large Language Models (LLMs) provide a robust theoretical foundation for AI agents, with the generation of reasoning plans being a crucial stage. Previous methods for generating reasoning plans primarily rely on In-Context Learning (ICL) or Supervised Fine-Tuning (SFT). However, methods based on ICL struggle with accurately utilizing tools, while those based on SFT face challenges in adapting to new toolsets and tasks. To address these issues, we introduce ToolFiVe, a general, plug-and-play, self-correction-based framework for leveraging specialized tools in compositional reasoning tasks. ToolFiVe redesigns the process of reasoning plan generation and integrates the tool-execution stage. Specifically, in the reasoning plan generation stage, ToolFiVe employs a filtering module to exclude task-irrelevant tools, creating a candidate toolset. ToolFiVe then constructs prompts using the candidate toolset and iteratively generates and refines the reasoning plan. Subsequently, a verification module evaluates the completeness of the reasoning plan, providing feedback for the self-correction loop. Ultimately, the reasoning plan developed during this stage guides the execution of the tools. Extensive experiments demonstrate that ToolFiVe outperforms other state-of-the-art (SOTA) methods, highlighting the significance of reasoning plan generation for general tool-augmented LLMs.

Event Extraction (EE) is a challenging task that aims to extract structural event-related information from unstructured text. Traditional methods for EE depend on manual annotations, which are both expensive and scarce. Furthermore, the existing datasets mostly follow the long-tail distribution, severely hindering the previous methods of modeling tail types. Two techniques can address this issue: transfer learning and data generation. However, the existing methods based on transfer learning still rely on pre-training with a large amount of labeled data in the source domain. Additionally, the quality of data generated by previous data generation methods is difficult to control. In this paper, leveraging Large Language Models (LLMs), we propose novel methods for event extraction and generation based on dialogues, overcoming the problems of relying on source domain data and maintaining data quality. Specifically, this paper innovatively transforms the EE task into multi-turn dialogues, guiding LLMs to learn event schemas from historical dialogue information and output structural events. Furthermore, we introduce a novel LLM-based method for generating high-quality data, significantly improving traditional models’ performance with various paradigms and structures, especially on tail types. Adequate experiments on real-world datasets demonstrate the effectiveness of the proposed event extraction and data generation methods.

Pre-training Event Extraction (EE) models on unlabeled data is an effective strategy that frees researchers from costly and labor-intensive data annotation. However, existing pre-training methods necessitate substantial computational resources, requiring high-performance hardware infrastructure and extensive training duration. In response to these challenges, this paper proposes a Lighter, Faster, and more Data-efficient pre-training framework for EE, named LFDe. Distinct from existing methods that strive to establish a comprehensive representation space during pre-training, our framework focuses on quickly familiarizing with the task format from a small amount of automatically constructed pseudo-events. It comprises three stages: weak-label data construction, pre-training, and fine-tuning. Specifically, during the first stage, LFDe first automatically designates pseudo-triggers and arguments based on the characteristics of real events to form pre-training samples. In the processes of pre-training and fine-tuning, the framework reframes EE as the identification of tokens semantically closest to the prompt within the given sentence. This paper also introduces a novel prompt-based sequence labeling model for EE to accommodate this reframing. Experiments on real-world datasets show that compared to similar models, our framework requires fewer pre-training data (only about 0.04%), a shorter pre-training period (about 0.03%), and lower memory requirements (about 57.6%). Simultaneously, our framework significantly improves performance in various data-scarce scenarios.

Prompt learning is an effective paradigm that bridges gaps between the pre-training tasks and the corresponding downstream applications. Approaches based on this paradigm have achieved great transcendent results in various applications. However, it still needs to be answered how to design a general-purpose framework based on the prompt learning paradigm for various information extraction tasks. In this article, we propose a novel composable prompt-based generative framework, which could be applied to a wide range of tasks in the field of information extraction. Specifically, we reformulate information extraction tasks into the form of filling slots in pre-designed type-specific prompts, which consist of one or multiple sub-prompts. A strategy of constructing composable prompts is proposed to enhance the generalization ability in data-scarce scenarios. Furthermore, to fit this framework, we transform relation extraction into the task of determining semantic consistency in prompts. The experimental results demonstrate that our approach surpasses compared baselines on real-world datasets in data-abundant and data-scarce scenarios. Further analysis of the proposed framework is presented, as well as numerical experiments conducted to investigate impact factors of performance on various tasks.

Temporal knowledge graph (TKG) reasoning that infers missing facts in the future is an essential and challenging task. When predicting a future event, there must be a narrative evolutionary process composed of closely related historical facts to support the event's occurrence, namely fact precursors. However, most existing models employ a sequential reasoning process in an auto-regressive manner, which cannot capture precursor information. This paper proposes a novel auto-encoder architecture that introduces a relation-aware graph attention layer into transformer (rGalT) to accommodate inference over the TKG. Specifically, we first calculate the correlation between historical and predicted facts through multiple attention mechanisms along intra-graph and inter-graph dimensions, then constitute these mutually related facts into diverse fact segments. Next, we borrow the translation generation idea to decode in parallel the precursor information associated with the given query, which enables our model to infer future unknown facts by progressively generating graph structures. Experimental results on four benchmark datasets demonstrate that our model outperforms other state-of-the-art methods, and precursor identification provides supporting evidence for prediction.

Conventional deep learning-based Relation Classification (RC) methods heavily rely on large-scale training dataset and fail to generalize to unseen classes when training data is scant. This work concentrates on RC tasks in few-shot scenarios in which models classify the unlabelled samples given only few labeled samples. Existing few-shot RC models consider the dataset as a series of individual instances and have not fully utilized interaction information among them. Interaction information is conducive to indicate the important areas and produce discriminating representations. So this paper proposes a novel interactive attention network (IAN) which uses inter-instance and intra-instance interactive information to classify the relations. Inter-instance interactive information is first introduced to solve the low-resource problem by capturing the semantic relevance between an instance pair. Intra-instance interactive information is then introduced to address the ambiguous relation classification issue by extracting the entity information inner an instance. Extensive numerical experimental results demonstrate the proposed method promotes the accuracy of down-stream task.

Event extraction is an important subtask of information extraction. The goal of event extraction is to quickly extract events of a specified type from a large amount of textual information. Many excellent models and algorithms have been proposed since ACE released the event extraction task in 2005. Most of them are based on the dataset published by ACE and have contributed to the accuracy of event extraction to a certain extent. In practical applications, the processing object of the event extraction task is large-scale text data. However, as far as we know, there is currently no effective model for using multiple computers for event extraction. In this paper, we propose a model for event extraction based on inter-cloud computing technology. The experimental results prove that our method reduces the time consumption and also gets better accuracy than advanced models.

Traditional approaches to the task of ACE event extraction usually depend on manually annotated data, which is often laborious to create and limited in size. Therefore, in addition to the difficulty of event extraction itself, insufficient training data hinders the learning process as well. To promote event extraction, we first propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. Moreover, to address the problem of insufficient training data, we propose a method to automatically generate labeled data by editing prototypes and screen out generated samples by ranking the quality. Experiments on the ACE2005 dataset demonstrate that our extraction model can surpass most existing extraction methods. Besides, incorporating our generation method exhibits further significant improvement. It obtains new state-of-the-art results on the event extraction task, including pushing the F1 score of trigger classification to 81.1%, and the F1 score of argument classification to 58.9%.

We present an original end-to-end available bandwidth measurement method, called SMART (statistics measurement for avail-bw by random train). It resolves some of the problems common for many types of existing probing methods, e.g. the long latency and large probe traffic. Contrary to traditional estimates of available bandwidth, SMART is not a methodology based on packet dispersion in packet pair or packet train, but a completely new methodology in the light of probability and statistics. The fundamental idea is to send very small packets at random moment and calculate the proportion of minimal delay ion total test samples. To reach this purpose, we redefine the available bandwidth based on probability and statistics. We have evaluated our method in controlled and reproducible environment using NS2, and the simulations show our method is accurate, efficient, quick and non-intrusive.

In current research on mobile ad-hoc networks, test-beds are always needed. However, real test-beds are costly, and sometimes unfeasible, especially for large-scale mobile ad-hoc networks. Therefore, emulation systems are provided. Unfortunately, exiting emulation systems have obvious defects. They can hardly support scalability or real-time simultaneously, neither adding nor removing mobile node dynamically. In this paper, we propose a test system named ManTS. With distributed architecture and some novel approaches, such as direct transmittal and virtual application traffic, and others. ManTS is able to build test-beds for large-scale mobile ad-hoc networks and guarantee real-time emulation at the same time. Furthermore, IP or upper layer protocols and applications implemented on Linux can run on ManTS without any modification. And with ManTS, simulated mobile nodes can be added into and deleted from mobile ad-hoc networks freely, which is quite useable to test routing and transport protocols. In a word, ManTS is a more flexible test system, and really suitable to test IP and upper layer protocols and applications for large-scale mobile ad-hoc networks.

In the future, more and more services will be supplied over networks, which can bring end users great pleasure and convenience. It can become true if service providers can benefit from these services. For this purpose, authentication, authorization and accounting or AAA for short is needed. Unfortunately, current AAA architectures are designed without especially considering deploying new services for service providers and consuming various services for end users. In other words, these AAA architectures are not designed from aspects of service providers and service consumers both. To remedy this problem, a novel service-oriented AAA architecture is proposed in this paper. It introduces a novel AAA component named AAA agent, which resides in service equipments as a logical component. AAA agent provides a standard interface for the component that provides a specific service, namely service-providing server, and the interface has nothing to do with AAA details. It is AAA agent that deals with all AAA affairs for the service-proving server. Therefore all AAA details are hidden from service-proving server. Besides, a user credential mechanism is also proposed to enable users to roam among various services seamlessly only if they have rights. In a word, the proposed service-oriented AAA architecture is a novel AAA architecture that enables service providers to deploy new services easily and helps users to enjoy different services conveniently.