Cognitive rehabilitation, STEM skill acquisition, and coaching games such as chess often require tutoring decision-making strategies. The advancement of AI-driven tutoring systems for facilitating human learning requires an understanding of the impact of evaluative feedback on human decision-making and skill development. To this end, we conduct human experiments using Amazon Mechanical Turk to study the influence of evaluative feedback on human decision-making in sequential tasks. In these experiments, participants solve the Tower of Hanoi puzzle and receive AI-generated feedback while solving it. We examine how this feedback affects their learning and skill transfer to related tasks. We also explore various computational models to understand how people incorporate evaluative feedback into their decision-making processes.
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The rise in additive manufacturing comes with unique opportunities and challenges. Rapid changes to part design and massive part customization distinctive to 3D-Print (3DP) can be easily achieved. Customized parts that are unique, yet exhibit similar features such as dental moulds, shoe insoles, or engine vanes could be industrially manufactured with 3DP. However, the opportunity for massive part customization comes with unique challenges for the existing production paradigm of robotics applications, as the current robotics paradigm for part identification and pose refinement is repetitive, where data-driven and object-dependent approaches are often used. Thus, a bottleneck exists in robotics applications for 3DP parts where massive customization is involved, as it is difficult for feature-based deep learning approaches to distinguish between similar parts such as shoe insoles belonging to different people. As such, we propose a method that augments patterns on 3DP parts so that grasping, part identification, and pose refinement can be executed in one shot with a tactile gripper. We also experimentally evaluate our approach from three perspectives, including real insertion tasks that mimic robotic sorting and packing, and achieved excellent classification results, a high insertion success rate of 95%, and a sub-millimeter pose refinement accuracy.
Automatic program repair (APR) techniques have the potential to reduce manual efforts in uncovering and repairing program defects during the code review (CR) process. However, the limited accuracy and considerable time costs associated with existing APR approaches hinder their adoption in industrial practice. One key factor is the under-utilization of review comments, which provide valuable insights into defects and potential fixes. Recent advancements in Large Language Models (LLMs) have enhanced their ability to comprehend natural and programming languages, enabling them to generate patches based on review comments. This paper conducts a comprehensive investigation into the effective utilization of LLMs for repairing CR defects. In this study, various prompts are designed and compared across mainstream LLMs using two distinct datasets from human reviewers and automated checkers. Experimental results demonstrate a remarkable repair rate of 72.97% with the best prompt, highlighting a substantial improvement in the effectiveness and practicality of automatic repair techniques.
We study the problem of screening in decision-making processes under uncertainty, focusing on the impact of adding an additional screening stage, commonly known as a 'gatekeeper.' While our primary analysis is rooted in the context of job market hiring, the principles and findings are broadly applicable to areas such as educational admissions, healthcare patient selection, and financial loan approvals. The gatekeeper's role is to assess applicants' suitability before significant investments are made. Our study reveals that while gatekeepers are designed to streamline the selection process by filtering out less likely candidates, they can sometimes inadvertently affect the candidates' own decision-making process. We explore the conditions under which the introduction of a gatekeeper can enhance or impede the efficiency of these processes. Additionally, we consider how adjusting gatekeeping strategies might impact the accuracy of selection decisions. Our research also extends to scenarios where gatekeeping is influenced by historical biases, particularly in competitive settings like hiring. We discover that candidates confronted with a statistically biased gatekeeping process are more likely to withdraw from applying, thereby perpetuating the previously mentioned historical biases. The study suggests that measures such as affirmative action can be effective in addressing these biases. While centered on hiring, the insights and methodologies from our study have significant implications for a wide range of fields where screening and gatekeeping are integral.
Instruction tuning, a specialized technique to enhance large language model (LLM) performance via instruction datasets, relies heavily on the quality of employed data. Existing quality improvement methods alter instruction data through dataset expansion or curation. However, the expansion method risks data redundancy, potentially compromising LLM performance, while the curation approach confines the LLM's potential to the original dataset. Our aim is to surpass the original data quality without encountering these shortcomings. To achieve this, we propose LIFT (LLM Instruction Fusion Transfer), a novel and versatile paradigm designed to elevate the instruction quality to new heights. LIFT strategically broadens data distribution to encompass more high-quality subspaces and eliminates redundancy, concentrating on high-quality segments across overall data subspaces. Experimental results demonstrate that, even with a limited quantity of high-quality instruction data selected by our paradigm, LLMs not only consistently uphold robust performance across various tasks but also surpass some state-of-the-art results, highlighting the significant improvement in instruction quality achieved by our paradigm.
The rapid accumulation of Earth observation data presents a formidable challenge for the processing capabilities of traditional remote sensing desktop software, particularly when it comes to analyzing expansive geographical areas and prolonged temporal sequences. Cloud computing has emerged as a transformative solution, surmounting the barriers traditionally associated with the management and computation of voluminous datasets. This paper introduces the Analytical Insight of Earth (AI Earth), an innovative remote sensing intelligent computing cloud platform, powered by the robust Alibaba Cloud infrastructure. AI Earth provides an extensive collection of publicly available remote sensing datasets, along with a suite of computational tools powered by a high-performance computing engine. Furthermore, it provides a variety of classic deep learning (DL) models and a novel remote sensing large vision segmentation model tailored to different recognition tasks. The platform enables users to upload their unique samples for model training and to deploy third-party models, thereby increasing the accessibility and openness of DL applications. This platform will facilitate researchers in leveraging remote sensing data for large-scale applied research in areas such as resources, environment, ecology, and climate.
Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e.g.,} social network analysis and recommender systems), computer vision (\emph{e.g.,} object detection and point cloud learning), and natural language processing (\emph{e.g.,} relation extraction and sequence learning), to name a few. With the emergence of Transformers in natural language processing and computer vision, graph Transformers embed a graph structure into the Transformer architecture to overcome the limitations of local neighborhood aggregation while avoiding strict structural inductive biases. In this paper, we present a comprehensive review of GNNs and graph Transformers in computer vision from a task-oriented perspective. Specifically, we divide their applications in computer vision into five categories according to the modality of input data, \emph{i.e.,} 2D natural images, videos, 3D data, vision + language, and medical images. In each category, we further divide the applications according to a set of vision tasks. Such a task-oriented taxonomy allows us to examine how each task is tackled by different GNN-based approaches and how well these approaches perform. Based on the necessary preliminaries, we provide the definitions and challenges of the tasks, in-depth coverage of the representative approaches, as well as discussions regarding insights, limitations, and future directions.
Pre-trained Language Models (PLMs) have achieved great success in various Natural Language Processing (NLP) tasks under the pre-training and fine-tuning paradigm. With large quantities of parameters, PLMs are computation-intensive and resource-hungry. Hence, model pruning has been introduced to compress large-scale PLMs. However, most prior approaches only consider task-specific knowledge towards downstream tasks, but ignore the essential task-agnostic knowledge during pruning, which may cause catastrophic forgetting problem and lead to poor generalization ability. To maintain both task-agnostic and task-specific knowledge in our pruned model, we propose ContrAstive Pruning (CAP) under the paradigm of pre-training and fine-tuning. It is designed as a general framework, compatible with both structured and unstructured pruning. Unified in contrastive learning, CAP enables the pruned model to learn from the pre-trained model for task-agnostic knowledge, and fine-tuned model for task-specific knowledge. Besides, to better retain the performance of the pruned model, the snapshots (i.e., the intermediate models at each pruning iteration) also serve as effective supervisions for pruning. Our extensive experiments show that adopting CAP consistently yields significant improvements, especially in extremely high sparsity scenarios. With only 3% model parameters reserved (i.e., 97% sparsity), CAP successfully achieves 99.2% and 96.3% of the original BERT performance in QQP and MNLI tasks. In addition, our probing experiments demonstrate that the model pruned by CAP tends to achieve better generalization ability.
The dominating NLP paradigm of training a strong neural predictor to perform one task on a specific dataset has led to state-of-the-art performance in a variety of applications (eg. sentiment classification, span-prediction based question answering or machine translation). However, it builds upon the assumption that the data distribution is stationary, ie. that the data is sampled from a fixed distribution both at training and test time. This way of training is inconsistent with how we as humans are able to learn from and operate within a constantly changing stream of information. Moreover, it is ill-adapted to real-world use cases where the data distribution is expected to shift over the course of a model's lifetime. The first goal of this thesis is to characterize the different forms this shift can take in the context of natural language processing, and propose benchmarks and evaluation metrics to measure its effect on current deep learning architectures. We then proceed to take steps to mitigate the effect of distributional shift on NLP models. To this end, we develop methods based on parametric reformulations of the distributionally robust optimization framework. Empirically, we demonstrate that these approaches yield more robust models as demonstrated on a selection of realistic problems. In the third and final part of this thesis, we explore ways of efficiently adapting existing models to new domains or tasks. Our contribution to this topic takes inspiration from information geometry to derive a new gradient update rule which alleviate catastrophic forgetting issues during adaptation.
Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e.g., Rock-Paper-Scissors). Yet, there is a lack of rigorous treatment for defining diversity and constructing diversity-aware learning dynamics. In this work, we offer a geometric interpretation of behavioural diversity in games and introduce a novel diversity metric based on \emph{determinantal point processes} (DPP). By incorporating the diversity metric into best-response dynamics, we develop \emph{diverse fictitious play} and \emph{diverse policy-space response oracle} for solving normal-form games and open-ended games. We prove the uniqueness of the diverse best response and the convergence of our algorithms on two-player games. Importantly, we show that maximising the DPP-based diversity metric guarantees to enlarge the \emph{gamescape} -- convex polytopes spanned by agents' mixtures of strategies. To validate our diversity-aware solvers, we test on tens of games that show strong non-transitivity. Results suggest that our methods achieve much lower exploitability than state-of-the-art solvers by finding effective and diverse strategies.
State-of-the-art Convolutional Neural Network (CNN) benefits a lot from multi-task learning (MTL), which learns multiple related tasks simultaneously to obtain shared or mutually related representations for different tasks. The most widely-used MTL CNN structure is based on an empirical or heuristic split on a specific layer (e.g., the last convolutional layer) to minimize different task-specific losses. However, this heuristic sharing/splitting strategy may be harmful to the final performance of one or multiple tasks. In this paper, we propose a novel CNN structure for MTL, which enables automatic feature fusing at every layer. Specifically, we first concatenate features from different tasks according to their channel dimension, and then formulate the feature fusing problem as discriminative dimensionality reduction. We show that this discriminative dimensionality reduction can be done by 1x1 Convolution, Batch Normalization, and Weight Decay in one CNN, which we refer to as Neural Discriminative Dimensionality Reduction (NDDR). We perform ablation analysis in details for different configurations in training the network. The experiments carried out on different network structures and different task sets demonstrate the promising performance and desirable generalizability of our proposed method.
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