Large language models (LLMs) often produce inaccurate or misleading content-hallucinations. To address this challenge, we introduce Noise-Augmented Fine-Tuning (NoiseFiT), a novel framework that leverages adaptive noise injection based on the signal-to-noise ratio (SNR) to enhance model robustness. In particular, NoiseFiT selectively perturbs layers identified as either high-SNR (more robust) or low-SNR (potentially under-regularized) using a dynamically scaled Gaussian noise. We further propose a hybrid loss that combines standard cross-entropy, soft cross-entropy, and consistency regularization to ensure stable and accurate outputs under noisy training conditions. Our theoretical analysis shows that adaptive noise injection is both unbiased and variance-preserving, providing strong guarantees for convergence in expectation. Empirical results on multiple test and benchmark datasets demonstrate that NoiseFiT significantly reduces hallucination rates, often improving or matching baseline performance in key tasks. These findings highlight the promise of noise-driven strategies for achieving robust, trustworthy language modeling without incurring prohibitive computational overhead. Given the comprehensive and detailed nature of our experiments, we have publicly released the fine-tuning logs, benchmark evaluation artifacts, and source code online at W&B, Hugging Face, and GitHub, respectively, to foster further research, accessibility and reproducibility.

Deep learning algorithms, especially Transformer-based models, have achieved significant performance by capturing long-range dependencies and historical information. However, the power of convolution has not been fully investigated. Moreover, most existing works ignore the dynamic interaction among variables and evolutionary noise in series. Addressing these issues, we propose a Hierarchical Memorizing Network (HMNet). In particular, a hierarchical convolution structure is introduced to extract the information from the series at various scales. Besides, we propose a dynamic variable interaction module to learn the varying correlation and an adaptive denoising module to search and exploit similar patterns to alleviate noises. These modules can cooperate with the hierarchical structure from the perspective of fine to coarse grain. Experiments on five benchmarks demonstrate that HMNet significantly outperforms the state-of-the-art models by 10.6% on MSE and 5.7% on MAE. Our code is released at https://github.com/yzhHoward/HMNet.

Figure/ground assignment, in which the visual image is divided into nearer (figural) and farther (ground) surfaces, is an essential step in visual processing, but its underlying computational mechanisms are poorly understood. Figural assignment (often referred to as border ownership) can vary along a contour, suggesting a spatially distributed process whereby local and global cues are combined to yield local estimates of border ownership. In this paper we model figure/ground estimation in a Bayesian belief network, attempting to capture the propagation of border ownership across the image as local cues (contour curvature and T-junctions) interact with more global cues to yield a figure/ground assignment. Our network includes as a nonlocal factor skeletal (medial axis) structure, under the hypothesis that medial structure draws'' border ownership so that borders are owned by their interiors. We also briefly present a psychophysical experiment in which we measured local border ownership along a contour at various distances from an inducing cue (a T-junction).

Generating point clouds from structures is a highly valued method to control the generation of point clouds.One of the major problems in structure-based controllable point cloud generation is the lack of controllability to details, as details are missing in most existing representations of structures.It can be observed that definitions of details and structures are subjective.Details can be treated as structures on small scale.To represent structures in different scales at the same time, we present a graph-based representation of structures called the Multiscale Structure Graph(MSG).By treating details as small-scale structures, similar patterns of local structures can be found at different scales, places, densities, and angles.The knowledge learned from a pattern can be transferred to similar patterns in other scales.An encoding and generation mechanism, namely the Multiscale Structure-based Point Cloud Generator(MSPCG), for generating dense point clouds from the MSG is proposed, which can simultaneously learn local patterns with miscellaneous spatial properties.Our MSPCG also has great generalization ability and scalability.An MSPCG trained on the ShapeNet dataset can enable multi-scale edition on point clouds, generate point clouds for unseen categories, and generate indoor scenes from a given structure. The experimental results show that our method significantly outperforms baseline methods.

How much do managers know about how their teams work? We recently ran a research study involving 14 teams comprising 283 employees in four Fortune 500 companies. When managers were asked about their teams' work, on average they either did not know or could not remember 60% of the work their teams do. In one extreme instance, a manager in our study could describe only 4% of their team's work. The cost of managers not knowing this gap exists can be high, even in teams as small as five members, and is therefore applicable to any company, big or small.

Jennifer: And… What happens when toys are connected to the internet and kids can ask them anything.. and they'll not only answer back…. Jennifer: I'm Jennifer Strong and this episode, we explore the relationship AI has with kids. Judith: And we've been trying to find out if kids have similar kinds of intuitions about the devices as they do about the internet in general but we are seeing similar patterns with young children where again, young children given the choice are saying, I would rather go ask a person for information at least when they information has to do with facts. Jennifer: Alexa has more than 80,000 skills In the U.S.… and there's developer support for skills aimed at kids. Jennifer: And then there's a game called Kids Court.

High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining. These representations have different degrees of interpretability, with efficient distributed representations coming at the cost of the loss of feature to dimension mapping. This implies that there is obfuscation in the way concepts are captured in these embedding spaces. Its effects are seen in many representations and tasks, one particularly problematic one being in language representations where the societal biases, learned from underlying data, are captured and occluded in unknown dimensions and subspaces. As a result, invalid associations (such as different races and their association with a polar notion of good versus bad) are made and propagated by the representations, leading to unfair outcomes in different tasks where they are used. This work addresses some of these problems pertaining to the transparency and interpretability of such representations. A primary focus is the detection, quantification, and mitigation of socially biased associations in language representation.

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters, in the long run, is the leveraging of computation. These two need not run counter to each other, but in practice, they tend to.

Figure/ground assignment, in which the visual image is divided into nearer (figural) and farther (ground) surfaces, is an essential step in visual processing, but its underlying computational mechanisms are poorly understood. Figural assignment (often referred to as border ownership) can vary along a contour, suggesting a spatially distributed process whereby local and global cues are combined to yield local estimates of border ownership. In this paper we model figure/ground estimation in a Bayesian belief network, attempting to capture the propagation of border ownership across the image as local cues (contour curvature and T-junctions) interact with more global cues to yield a figure/ground assignment. Our network includes as a nonlocal factor skeletal (medial axis) structure, under the hypothesis that medial structure draws'' border ownership so that borders are owned by their interiors. We also briefly present a psychophysical experiment in which we measured local border ownership along a contour at various distances from an inducing cue (a T-junction).