Mathematical reasoning serves as a cornerstone for assessing the fundamental cognitive capabilities of human intelligence. In recent times, there has been a notable surge in the development of Large Language Models (LLMs) geared towards the automated resolution of mathematical problems. However, the landscape of mathematical problem types is vast and varied, with LLM-oriented techniques undergoing evaluation across diverse datasets and settings. This diversity makes it challenging to discern the true advancements and obstacles within this burgeoning field. This survey endeavors to address four pivotal dimensions: i) a comprehensive exploration of the various mathematical problems and their corresponding datasets that have been investigated; ii) an examination of the spectrum of LLM-oriented techniques that have been proposed for mathematical problem-solving; iii) an overview of factors and concerns affecting LLMs in solving math; and iv) an elucidation of the persisting challenges within this domain. To the best of our knowledge, this survey stands as one of the first extensive examinations of the landscape of LLMs in the realm of mathematics, providing a holistic perspective on the current state, accomplishments, and future challenges in this rapidly evolving field.

Prior work has uncovered a set of common problems in state-of-the-art context-based question answering (QA) systems: a lack of attention to the context when the latter conflicts with a model's parametric knowledge, little robustness to noise, and a lack of consistency with their answers. However, most prior work focus on one or two of those problems in isolation, which makes it difficult to see trends across them. We aim to close this gap, by first outlining a set of -- previously discussed as well as novel -- desiderata for QA models. We then survey relevant analysis and methods papers to provide an overview of the state of the field. The second part of our work presents experiments where we evaluate 15 QA systems on 5 datasets according to all desiderata at once. We find many novel trends, including (1) systems that are less susceptible to noise are not necessarily more consistent with their answers when given irrelevant context; (2) most systems that are more susceptible to noise are more likely to correctly answer according to a context that conflicts with their parametric knowledge; and (3) the combination of conflicting knowledge and noise can reduce system performance by up to 96%. As such, our desiderata help increase our understanding of how these models work and reveal potential avenues for improvements.

Traditional benchmarking methods are often used to evaluate algorithms in isolation, with a single algorithm configuration (hyper-parameter setting) or with a limited set of a few variations against a limited set of state-of-the-art algorithms, leading to limited insights into their comparative performance and practical applicability. This study addresses these challenges by employing modular optimization approaches and explainable AI techniques in order to derive insights into the algorithmic behaviour of a large set of algorithm components (modules) and their hyper-parameters. Modular optimization frameworks allow for the comparison of various modifications on a core algorithm, facilitating a deeper understanding of each component's influence on the algorithm's performance in different scenarios. There is already a wide variety of modular algorithm frameworks available, but their application for explicit explainability of the various algorithmic components and settings has been relatively unexplored. This paper aims to bridge this gap by providing a comprehensive framework for explainable benchmarking in iterative optimization heuristics and by providing a software library (IOH-Xplainer) to facilitate researchers to use the proposed framework.

The number of Language Models (LMs) dedicated to processing scientific text is on the rise. Keeping pace with the rapid growth of scientific LMs (SciLMs) has become a daunting task for researchers. To date, no comprehensive surveys on SciLMs have been undertaken, leaving this issue unaddressed. Given the constant stream of new SciLMs, appraising the state-of-the-art and how they compare to each other remain largely unknown. This work fills that gap and provides a comprehensive review of SciLMs, including an extensive analysis of their effectiveness across different domains, tasks and datasets, and a discussion on the challenges that lie ahead.

Autonomous manipulation in robot arms is a complex and evolving field of study in robotics. This paper proposes work stands at the intersection of two innovative approaches in the field of robotics and machine learning. Inspired by the Action Chunking with Transformer (ACT) model, which employs joint location and image data to predict future movements, our work integrates principles of Bilateral Control-Based Imitation Learning to enhance robotic control. Our objective is to synergize these techniques, thereby creating a more robust and efficient control mechanism. In our approach, the data collected from the environment are images from the gripper and overhead cameras, along with the joint angles, angular velocities, and forces of the follower robot using bilateral control. The model is designed to predict the subsequent steps for the joint angles, angular velocities, and forces of the leader robot. This predictive capability is crucial for implementing effective bilateral control in the follower robot, allowing for more nuanced and responsive maneuvering.

Natural Language Processing (NLP) aims to analyze the text via techniques in the computer science field. It serves the applications in healthcare, commerce, and education domains. Particularly, NLP has been applied to the education domain to help teaching and learning. In this survey, we review recent advances in NLP with a focus on solving problems related to the education domain. In detail, we begin with introducing the relevant background. Then, we present the taxonomy of NLP in the education domain. Next, we illustrate the task definition, challenges, and corresponding techniques based on the above taxonomy. After that, we showcase some off-the-shelf demonstrations in this domain and conclude with future directions.

Large Language Models (LLMs) have demonstrated remarkable capabilities in important tasks such as natural language understanding, language generation, and complex reasoning and have the potential to make a substantial impact on our society. Such capabilities, however, come with the considerable resources they demand, highlighting the strong need to develop effective techniques for addressing their efficiency challenges.In this survey, we provide a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from model-centric, data-centric, and framework-centric perspective, respectively. We have also created a GitHub repository where we compile the papers featured in this survey at https://github.com/AIoT-MLSys-Lab/Efficient-LLMs-Survey, and will actively maintain this repository and incorporate new research as it emerges. We hope our survey can serve as a valuable resource to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

Machine unlearning (MU) is gaining increasing attention due to the need to remove or modify predictions made by machine learning (ML) models. While training models have become more efficient and accurate, the importance of unlearning previously learned information has become increasingly significant in fields such as privacy, security, and fairness. This paper presents a comprehensive survey of MU, covering current state-of-the-art techniques and approaches, including data deletion, perturbation, and model updates. In addition, commonly used metrics and datasets are also presented. The paper also highlights the challenges that need to be addressed, including attack sophistication, standardization, transferability, interpretability, training data, and resource constraints. The contributions of this paper include discussions about the potential benefits of MU and its future directions. Additionally, the paper emphasizes the need for researchers and practitioners to continue exploring and refining unlearning techniques to ensure that ML models can adapt to changing circumstances while maintaining user trust. The importance of unlearning is further highlighted in making Artificial Intelligence (AI) more trustworthy and transparent, especially with the increasing importance of AI in various domains that involve large amounts of personal user data.

The first reviews of Apple's Vision Pro headset, from publications with early access to the company's attempt to create the next computing platform, talk of a big leap forward for face-mounted computers, for better or worse. The US-only headset, first announced in June last year, aims to move "spatial computing" beyond the limited mixed-reality offered by rivals from Meta, Microsoft and others. It is packed with cutting-edge technology including 3D cameras on the front to capture videos, the ability to blend the real and virtual worlds with hand and eye tracking, plus a display on the front that shows a simulacrum of the wearer's eyes. But at a cost of 3,499 (about 2,760) in the US it has a lot of work to do to convince consumers and developers alike that it can be anything other than a super-expensive niche toy for tech enthusiasts. The Verge's Nilay Patel called the Vision Pro an "astounding product" but one with a lot of big trade-offs, including messing up your hair each time you put it on: "Apple is very proud of the displays inside the Vision Pro, and for good reason – they represent a huge leap forward in display technology," he wrote.

This study explores modeling and control for quadrotor acrobatics, focusing on executing flip maneuvers. Flips are an elegant way to deliver sensor probes into no-fly or hazardous zones, like volcanic vents. Successful flips require feasible trajectories and precise control, influenced by rotor dynamics, thrust allocation, and control methodologies. The research introduces a novel approach using Model Predictive Control (MPC) for real-time trajectory planning. The MPC considers dynamic constraints and environmental variables, ensuring system stability during maneuvers. The proposed methodology's effectiveness is examined through simulation studies in ROS and Gazebo, providing insights into quadrotor behavior, response time, and trajectory accuracy. Real-time flight experiments on a custom agile quadrotor using PixHawk 4 and Hardkernel Odroid validate MPC-designed controllers. Experiments confirm successful execution and adaptability to real-world scenarios. Outcomes contribute to autonomous aerial robotics, especially aerial acrobatics, enhancing mission capabilities. MPC controllers find applications in probe throws and optimal image capture views through efficient flight paths, e.g., full roll maneuvers. This research paves the way for quadrotors in demanding scenarios, showcasing groundbreaking applications. Video Link: \url{ https://www.youtube.com/watch?v=UzR0PWjy9W4}