In RAG, a retrieval component is used to fetch relevant documents or data points, which are then used to inform This survey examines the most effective retrieval and enhance the generation process of the LLM. Similarly, algorithms utilized in ad recommendation and content in recommendation systems, retrieval methods serve to bring recommendation systems. Ad targeting algorithms forth pertinent user and item data, which can then be leveraged rely on detailed user profiles and behavioral by the recommendation algorithms to produce highly data to deliver personalized advertisements, personalized and accurate recommendations. This parallel thereby driving revenue through targeted placements.

A software requirement is a description of what the software will do and how it will be expected to perform [1]. Requirements vary in degree of detail, starting from business requirements, which are the highest levels of abstraction that focus on the general purpose of the target system, all the way up to software requirements, which precisely describe the characteristics and features required from the system [2]. Depending on these types, the task of requirements traceability varies between linking requirements from one level or linking requirements from different levels (such as linking business requirements to system requirements), which is called cross-level [2]. In general, requirements traceability is defined as "the ability to describe and follow the life of a requirement in both forwards and backward direction through periods of ongoing refinement and iteration" [3]. This task plays a crucial role in the success of software development so that the development team can better understand the needs of customers and system users, and thus be able to design and develop a system that effectively meets those requirements [4].

As Large Language Models (LLMs) become an important way of information seeking, there have been increasing concerns about the unethical content LLMs may generate. In this paper, we conduct a rigorous evaluation of LLMs' implicit bias towards certain groups by attacking them with carefully crafted instructions to elicit biased responses. Our attack methodology is inspired by psychometric principles in cognitive and social psychology. We propose three attack approaches, i.e., Disguise, Deception, and Teaching, based on which we built evaluation datasets for four common bias types. Each prompt attack has bilingual versions. Extensive evaluation of representative LLMs shows that 1) all three attack methods work effectively, especially the Deception attacks; 2) GLM-3 performs the best in defending our attacks, compared to GPT-3.5 and GPT-4; 3) LLMs could output content of other bias types when being taught with one type of bias. Our methodology provides a rigorous and effective way of evaluating LLMs' implicit bias and will benefit the assessments of LLMs' potential ethical risks.

With the rapid development of video Multimodal Large Language Models (MLLMs), numerous benchmarks have been proposed to assess their video understanding capability. However, due to the lack of rich events in the videos, these datasets may suffer from the short-cut bias that the answers can be deduced from a few frames, without the need to watch the entire video. To address this issue, we introduce Event-Bench, an event-oriented long video understanding benchmark built on existing datasets and human annotations. Event-Bench includes six event-related tasks and 2,190 test instances to comprehensively evaluate video event understanding ability. Additionally, we propose Video Instruction Merging~(VIM), a cost-effective method that enhances video MLLMs using merged, event-intensive video instructions, addressing the scarcity of human-annotated, event-intensive data. Extensive experiments show that the best-performing model, GPT-4o, achieves an overall accuracy of 53.33, significantly outperforming the best open-source model by 41.42%. Leveraging an effective instruction synthesis method and an adaptive model architecture, VIM surpasses both state-of-the-art open-source models and GPT-4V on the Event-Bench. All code, data, and models are publicly available at https://github.com/RUCAIBox/Event-Bench.

Domain adaptation aims to enable Large Language Models (LLMs) to generalize domain datasets unseen effectively during the training phase. However, factors such as the size of the model parameters and the scale of training data are general influencers and do not reflect the nuances of domain adaptation performance. This paper investigates the fine-grained factors affecting domain adaptation performance, analyzing the specific impact of `words' in training data on summarization tasks. We propose quantifying dataset learning difficulty as the learning difficulty of generative summarization, which is determined by two indicators: word-based compression rate and abstraction level. Our experiments conclude that, when considering dataset learning difficulty, the cross-domain overlap and the performance gain in summarization tasks exhibit an approximate linear relationship, which is not directly related to the number of words. Based on this finding, predicting a model's performance on unknown domain datasets is possible without undergoing training.

In the rapidly evolving landscape of the IoT, the security of connected devices has become a paramount concern. This paper explores the concept of proactive threat hunting as a pivotal strategy for enhancing the security and sustainability of IoT systems. Proactive threat hunting is an alternative to traditional reactive security measures that analyses IoT networks continuously and in advance to find and eliminate threats before they occure. By improving the security posture of IoT devices this approach significantly contributes to extending IoT operational lifespan and reduces environmental impact. By integrating security metrics similar to the Common Vulnerability Scoring System (CVSS) into consumer platforms, this paper argues that proactive threat hunting can elevate user awareness about the security of IoT devices. This has the potential to impact consumer choices and encourage a security-conscious mindset in both the manufacturing and user communities. Through a comprehensive analysis, this study demonstrates how proactive threat hunting can contribute to the development of a more secure, sustainable, and user-aware IoT ecosystem.

Speaker diarization provides the answer to the question "who spoke when?" for an audio file. This information can be used to complete audio transcripts for further processing steps. Most speaker diarization systems assume that the audio file is available as a whole. However, there are scenarios in which the speaker labels are needed immediately after the arrival of an audio segment. Speaker diarization with a correspondingly low latency is referred to as online speaker diarization. This paper provides an overview. First the history of online speaker diarization is briefly presented. Next a taxonomy and datasets for training and evaluation are given. In the sections that follow, online diarization methods and systems are discussed in detail. This paper concludes with the presentation of challenges that still need to be solved by future research in the field of online speaker diarization.

Meta-Reinforcement Learning (Meta-RL) aims to acquire meta-knowledge for quick adaptation to diverse tasks. However, applying these policies in real-world environments presents a significant challenge in balancing rapid adaptability with adherence to environmental constraints. Our novel approach, Constraint Model Agnostic Meta Learning (C-MAML), merges meta learning with constrained optimization to address this challenge. C-MAML enables rapid and efficient task adaptation by incorporating task-specific constraints directly into its meta-algorithm framework during the training phase. This fusion results in safer initial parameters for learning new tasks. We demonstrate the effectiveness of C-MAML in simulated locomotion with wheeled robot tasks of varying complexity, highlighting its practicality and robustness in dynamic environments.

Recently there has been a surge of interest in extending the success of large language models (LLMs) to graph modality, such as social networks and molecules. As LLMs are predominantly trained with 1D text data, most existing approaches adopt a graph neural network to represent a graph as a series of node tokens and feed these tokens to LLMs for graph-language alignment. Despite achieving some successes, existing approaches have overlooked the hierarchical structures that are inherent in graph data. Especially, in molecular graphs, the high-order structural information contains rich semantics of molecular functional groups, which encode crucial biochemical functionalities of the molecules. We establish a simple benchmark showing that neglecting the hierarchical information in graph tokenization will lead to subpar graph-language alignment and severe hallucination in generated outputs. To address this problem, we propose a novel strategy called HIerarchical GrapH Tokenization (HIGHT). HIGHT employs a hierarchical graph tokenizer that extracts and encodes the hierarchy of node, motif, and graph levels of informative tokens to improve the graph perception of LLMs. HIGHT also adopts an augmented graph-language supervised fine-tuning dataset, enriched with the hierarchical graph information, to further enhance the graph-language alignment. Extensive experiments on 7 molecule-centric benchmarks confirm the effectiveness of HIGHT in reducing hallucination by 40%, as well as significant improvements in various molecule-language downstream tasks.

Data contamination has garnered increased attention in the era of large language models (LLMs) due to the reliance on extensive internet-derived training corpora. The issue of training corpus overlap with evaluation benchmarks--referred to as contamination--has been the focus of significant recent research. This body of work aims to identify contamination, understand its impacts, and explore mitigation strategies from diverse perspectives. However, comprehensive studies that provide a clear pathway from foundational concepts to advanced insights are lacking in this nascent field. Therefore, we present a comprehensive survey in the field of data contamination, laying out the key issues, methodologies, and findings to date, and highlighting areas in need of further research and development. In particular, we begin by examining the effects of data contamination across various stages and forms. We then provide a detailed analysis of current contamination detection methods, categorizing them to highlight their focus, assumptions, strengths, and limitations. We also discuss mitigation strategies, offering a clear guide for future research. This survey serves as a succinct overview of the most recent advancements in data contamination research, providing a straightforward guide for the benefit of future research endeavors.