Video-Language Models (VLMs), powered by the advancements in Large Language Models (LLMs), are charting new frontiers in video understanding. A pivotal challenge is the development of an effective method to encapsulate video content into a set of representative tokens to align with LLMs. In this work, we introduce Slot-VLM, a new framework designed to generate semantically decomposed video tokens, in terms of object-wise and event-wise visual representations, to facilitate LLM inference. In order to take into account both the spatial object details and the varied temporal dynamics, we build OE-Slots with two branches: the Object-Slots branch and the Event-Slots branch. The Object-Slots branch focuses on extracting object-centric slots from features of high spatial resolution but low frame sample rate, emphasizing detailed object information.

In this paper, we introduce DeAR (Decompose-Analyze-Rethink), a framework that iteratively builds a reasoning tree to tackle intricate problems within a single large language model (LLM). Unlike approaches that extend or search for rationales, DeAR is featured by 1) adopting a tree-based question decomposition manner to plan the organization of rationales, which mimics the logical planning inherentin human cognition; 2) globally updating the rationales at each reasoning step through natural language feedback. Specifically, the Decompose stage decomposes the question into simpler sub-questions, storing them as new nodes; the Analyze stage generates and self-checks rationales for sub-questions at each node evel; and the Rethink stage updates parent-node rationales based on feedback from their child nodes. By generating and updating the reasoning process from a more global perspective, DeAR constructs more adaptive and accurate logical structures for complex problems, facilitating timely error correction compared to rationale-extension and search-based approaches such as Tree-of-Thoughts (ToT) and Graph-of-Thoughts (GoT). We conduct extensive experiments on three reasoning benchmarks, including ScienceQA, StrategyQA, and GSM8K, which cover a variety of reasoning tasks, demonstrating that our approach significantly reduces logical errors and enhances performance across various LLMs.

The recent development of chain-of-thought (CoT) decoding has enabled large language models (LLMs) to generate explicit logical reasoning paths for complex problem-solving. However, research indicates that these paths are not always deliberate and optimal. The tree-of-thought (ToT) method employs tree-searching to extensively explore the reasoning space and find better reasoning paths that CoT decoding might overlook. This deliberation, however, comes at the cost of significantly increased inference complexity. In this work, we demonstrate that fine-tuning LLMs leveraging the search tree constructed by ToT allows CoT to achieve similar or better performance, thereby avoiding the substantial inference burden.

Data distillation and coresets have emerged as popular approaches to generate a smaller representative set of samples for downstream learning tasks to handle large-scale datasets. At the same time, machine learning is being increasingly applied to decision-making processes at a societal level, making it imperative for modelers to address inherent biases towards subgroups present in the data. While current approaches focus on creating fair synthetic representative samples by optimizing local properties relative to the original samples, their impact on downstream learning processes has yet to be explored. In this work, we present fair Wasserstein coresets ( \texttt{FWC}), a novel coreset approach which generates fair synthetic representative samples along with sample-level weights to be used in downstream learning tasks. We show that an unconstrained version of \texttt{FWC} is equivalent to Lloyd's algorithm for k-medians and k-means clustering. Experiments conducted on both synthetic and real datasets show that \texttt{FWC}: (i) achieves a competitive fairness-performance tradeoff in downstream models compared to existing approaches, (ii) improves downstream fairness when added to the existing training data and (iii) can be used to reduce biases in predictions from large language models (GPT-3.5 and GPT-4).

Large Language Models (LLMs) are increasingly used for various tasks with graph structures. Though LLMs can process graph information in a textual format, they overlook the rich vision modality, which is an intuitive way for humans to comprehend structural information and conduct general graph reasoning. The potential benefits and capabilities of representing graph structures as visual images (i.e., \textit{visual graph}) are still unexplored. To fill the gap, we innovatively propose an end-to-end framework, called \textbf{G} raph to v \textbf{I} sual and \textbf{T} extual Integr \textbf{A} tion (GITA), which firstly incorporates visual graphs into general graph reasoning. Extensive experiments on the GVLQA dataset and five real-world datasets show that GITA outperforms mainstream LLMs in terms of general graph reasoning capabilities.

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OpenAI CEO Sam Altman sits down with Shannon Bream to discuss the positives and potential negatives of artificial intelligence and the importance of maintaining a lead in the AI industry over China. OpenAI has just made a move that's turning heads across the tech world. The company is acquiring io, the AI device startup founded by Jony Ive, for nearly 6.5 billion. It's a collaboration between Sam Altman, who leads OpenAI, and the designer responsible for some of Apple's most iconic products, including the iPhone and Apple Watch. Together, they want to create a new generation of AI-powered devices that could completely change how we use technology.

But quite recently, in the real world there has been a rapid tipping point in thinking on machine consciousness, where credible voices have become concerned that this is no longer the stuff of science fiction. The sudden shift has been prompted by the success of so-called large language models (LLMs), which can be accessed through apps on our phones such as Gemini and Chat GPT. The ability of the latest generation of LLMs to have plausible, free-flowing conversations has surprised even their designers and some of the leading experts in the field. There is a growing view among some thinkers that as AI becomes even more intelligent, the lights will suddenly turn on inside the machines and they will become conscious. Others, such as Prof Anil Seth who leads the Sussex University team, disagree, describing the view as "blindly optimistic and driven by human exceptionalism".

Millions of people rely on search functionality to find and explore content on entertainment platforms. Modern search systems use a combination of candidate generation and ranking approaches, with advanced methods leveraging deep learning and LLM-based techniques to retrieve, generate, and categorize search results. Despite these advancements, search algorithms can still surface inappropriate or irrelevant content due to factors like model unpredictability, metadata errors, or overlooked design flaws. Such issues can misalign with product goals and user expectations, potentially harming user trust and business outcomes. In this work, we introduce an additional monitoring layer using Large Language Models (LLMs) to enhance content moderation. This additional layer flags content if the user did not intend to search for it. This approach serves as a baseline for product quality assurance, with collected feedback used to refine the initial retrieval mechanisms of the search model, ensuring a safer and more reliable user experience.

Large language models (LLMs) offer unprecedented and growing capabilities, but also introduce complex safety and security challenges that resist conventional risk management. While conventional probabilistic risk analysis (PRA) requires exhaustive risk enu meration and quantification, the novelty and complexity of these systems make PRA impractical, particularly against adaptive adversaries. Previous research found that risk management in various fields of engineering such as nuclear or civil engineering is often solved by generic (i.e. Here we show how emerging risks in LLM - powered systems could be met with 100+ of these non - probabilistic strategies to risk management, including risks from adaptive adversaries. The strategies are divided into five categories and are mapped to LLM secur ity (and AI safety more broadly). We also present an LLM - powered workflow for applying these strategies and other workflows suitable for solution architec ts. Overall, these strategies could contribute (despite some limitations) to security, safety and other dimensions of responsible AI.