For the past couple of weeks, I've been making a home video on my phone, using Apple's iMovie software. The idea is to weave together clips of my family that I've taken during the month of February; I plan to keep working on it until March. So far, the movie shows my five-month-old daughter cooing and waving her arms; my five-year-old son chasing me with a snowball; and a visit to the spooky, run-down amusement park in our town, among other things. I thought of my movie while absorbing the announcement, yesterday, of Sora, an astonishing new text-to-video system from OpenAI, the makers of ChatGPT. Sora can take prompts from users and produce detailed, inventive, and photorealistic one-minute-long videos.

Rep. Ronny Jackson reiterated his calls for President Biden to prove his mental fitness for office after it was put into question by Special Counsel Robert Hur. FIRST ON FOX: House Republicans are appealing directly to President Biden demanding that he take a cognitive test to prove his mental fitness for office. Rep. Ronny Jackson, R-Texas, the former White House physician who served as chief medical adviser to former President Trump, led a letter to the president co-signed by 83 House Republicans, including House GOP Conference Chair Elise Stefanik and Chief Deputy Whip Guy Reschenthaler, arguing that the president's many public "gaffes" are a "national security concern." "Following the recent report from Special Counsel Robert Hur, we write to express our grave concerns with your current cognitive state and ability to successfully execute the duties of the Presidency, including as Chief Executive, Head of State, and Commander in Chief," the lawmakers wrote. "The President of the United States must demonstrate sound mental abilities, regardless of gender, age, or political party, which you have not." Texas GOP Rep. Ronny Jackson, a former White House physician, left, is again calling on President Biden to take a cognitive exam.

One major criticism of deep learning centers around the biological implausibility of the credit assignment schema used for learning -- backpropagation of errors. This implausibility translates into practical limitations, spanning scientific fields, including incompatibility with hardware and non-differentiable implementations, thus leading to expensive energy requirements. In contrast, biologically plausible credit assignment is compatible with practically any learning condition and is energy-efficient. As a result, it accommodates hardware and scientific modeling, e.g. learning with physical systems and non-differentiable behavior. Furthermore, it can lead to the development of real-time, adaptive neuromorphic processing systems. In addressing this problem, an interdisciplinary branch of artificial intelligence research that lies at the intersection of neuroscience, cognitive science, and machine learning has emerged. In this paper, we survey several vital algorithms that model bio-plausible rules of credit assignment in artificial neural networks, discussing the solutions they provide for different scientific fields as well as their advantages on CPUs, GPUs, and novel implementations of neuromorphic hardware. We conclude by discussing the future challenges that will need to be addressed in order to make such algorithms more useful in practical applications.

Decision trees built with data remain in widespread use for nonparametric prediction. Predicting probability distributions is preferred over point predictions when uncertainty plays a prominent role in analysis and decision-making. We study modifying a tree to produce nonparametric predictive distributions. We find the standard method for building trees may not result in good predictive distributions and propose changing the splitting criteria for trees to one based on proper scoring rules. Analysis of both simulated data and several real datasets demonstrates that using these new splitting criteria results in trees with improved predictive properties considering the entire predictive distribution.

Tool learning is widely acknowledged as a foundational approach or deploying large language models (LLMs) in real-world scenarios. While current research primarily emphasizes leveraging tools to augment LLMs, it frequently neglects emerging safety considerations tied to their application. To fill this gap, we present $ToolSword$, a comprehensive framework dedicated to meticulously investigating safety issues linked to LLMs in tool learning. Specifically, ToolSword delineates six safety scenarios for LLMs in tool learning, encompassing $malicious$ $queries$ and $jailbreak$ $attacks$ in the input stage, $noisy$ $misdirection$ and $risky$ $cues$ in the execution stage, and $harmful$ $feedback$ and $error$ $conflicts$ in the output stage. Experiments conducted on 11 open-source and closed-source LLMs reveal enduring safety challenges in tool learning, such as handling harmful queries, employing risky tools, and delivering detrimental feedback, which even GPT-4 is susceptible to. Moreover, we conduct further studies with the aim of fostering research on tool learning safety. The data is released in https://github.com/Junjie-Ye/ToolSword.

Previous STRIPS domain model acquisition approaches that learn from state traces start with the names and parameters of the actions to be learned. Therefore their only task is to deduce the preconditions and effects of the given actions. In this work, we explore learning in situations when the parameters of learned actions are not provided. We define two levels of trace quality based on which information is provided and present an algorithm for each. In one level (L1), the states in the traces are labeled with action names, so we can deduce the number and names of the actions, but we still need to work out the number and types of parameters. In the other level (L2), the states are additionally labeled with objects that constitute the parameters of the corresponding grounded actions. Here we still need to deduce the types of the parameters in the learned actions. We experimentally evaluate the proposed algorithms and compare them with the state-of-the-art learning tool FAMA on a large collection of IPC benchmarks. The evaluation shows that our new algorithms are faster, can handle larger inputs and provide better results in terms of learning action models more similar to reference models.

The development of state-of-the-art generative large language models (LLMs) disproportionately relies on English-centric tokenizers, vocabulary and pre-training data. Despite the fact that some LLMs have multilingual capabilities, recent studies have shown that their inference efficiency deteriorates when generating text in languages other than English. This results in increased inference time and costs. Cross-lingual vocabulary adaptation methods have been proposed for adapting models to a target language aiming to improve downstream performance. However, the effectiveness of these methods on increasing inference efficiency of generative LLMs has yet to be explored. In this paper, we perform an empirical study of various cross-lingual vocabulary adaptation methods on five generative LLMs (including monolingual and multilingual models) across four typologically-diverse languages and four natural language understanding tasks. We find that cross-lingual vocabulary adaptation substantially contributes to LLM inference speedups of up to 271.5%. We also show that adapting LLMs that have been pre-trained on more balanced multilingual data results in downstream performance comparable to the original models.

Despite recent progress, large language models (LLMs) still face the challenge of appropriately reacting to the intricacies of social and cultural conventions. This paper presents MANGO, a methodology for distilling high-accuracy, high-recall assertions of cultural knowledge. We judiciously and iteratively prompt LLMs for this purpose from two entry points, concepts and cultures. Outputs are consolidated via clustering and generative summarization. Running the MANGO method with GPT-3.5 as underlying LLM yields 167K high-accuracy assertions for 30K concepts and 11K cultures, surpassing prior resources by a large margin. For extrinsic evaluation, we explore augmenting dialogue systems with cultural knowledge assertions. We find that adding knowledge from MANGO improves the overall quality, specificity, and cultural sensitivity of dialogue responses, as judged by human annotators. Data and code are available for download.

We study the estimation of a planted signal hidden in a recently introduced nested matrix-tensor model, which is an extension of the classical spiked rank-one tensor model, motivated by multi-view clustering. Prior work has theoretically examined the performance of a tensor-based approach, which relies on finding a best rank-one approximation, a problem known to be computationally hard. A tractable alternative approach consists in computing instead the best rank-one (matrix) approximation of an unfolding of the observed tensor data, but its performance was hitherto unknown. We quantify here the performance gap between these two approaches, in particular by deriving the precise algorithmic threshold of the unfolding approach and demonstrating that it exhibits a BBP-type transition behavior (Baik et al., 2005). This work is therefore in line with recent contributions which deepen our understanding of why tensor-based methods surpass matrix-based methods in handling structured tensor data. In the age of artificial intelligence, handling vast amounts of data has become a fundamental aspect of machine learning tasks. Datasets are often high-dimensional and composed of multiple modes, such as various modalities, sensors, sources, types, or domains, naturally lending themselves to be represented as tensors. Tensors offer a richer structure compared to traditional one-dimensional vectors and two-dimensional matrices, making them increasingly relevant in various applications, including statistical learning and data analysis (Landsberg, 2012; Sun et al., 2014). Y et, in the existing literature, there is a notable scarcity of theoretical studies that specifically address the performance gaps between tensor-based methods and traditional (matrix) spectral methods in the context of high-dimensional data analysis. While tensor methods have shown promise in various applications, including multi-view clustering, co-clustering, community detection, and latent variable modeling (Wu et al., 2019; Anandkumar et al., 2014; Papalexakis et al., 2012; Wang et al., 2023), little attention has been devoted to rigorously quantifying the advantages and drawbacks of leveraging the hidden low-rank tensor structure.

Hallucinations pose a significant challenge for the practical implementation of large language models (LLMs). The utilization of parametric knowledge in generating factual content is constrained by the limited knowledge of LLMs, potentially resulting in internal hallucinations. While incorporating external information can help fill knowledge gaps, it also introduces the risk of irrelevant information, thereby increasing the likelihood of external hallucinations. A careful and balanced integration of the parametric knowledge within LLMs with external information is crucial to alleviate hallucinations. In this study, we present Rowen, a novel approach that enhances LLMs with a selective retrieval augmentation process tailored to address hallucinated outputs. This process is governed by a multilingual semantic-aware detection module, which evaluates the consistency of the perturbed responses across various languages for the same queries. Upon detecting inconsistencies indicative of hallucinations, Rowen activates the retrieval of external information to rectify the model outputs. Rowen adeptly harmonizes the intrinsic parameters in LLMs with external knowledge sources, effectively mitigating hallucinations by ensuring a balanced integration of internal reasoning and external evidence. Through a comprehensive empirical analysis, we demonstrate that Rowen surpasses the current state-of-the-art in both detecting and mitigating hallucinated content within the outputs of LLMs.