OpenRaiser/Intern-Atlas huggingface dataset:
Causality Analysis for Evaluating the Security of Large Language Models
Venue: arXiv cs.AI 2023
Year: 2023

Large Language Models (LLMs) such as GPT and Llama2 are increasingly adopted in many safety-critical applications. Their security is thus essential. Even with considerable efforts spent on reinforcement learning from human feedback (RLHF), recent studies have shown that LLMs are still subject to attacks such as adversarial perturbation and Trojan attacks. Further research is thus needed to evaluate their security and/or understand the lack of it. In this work, we propose a framework for conducting light-weight causality-analysis of LLMs at the token, layer, and neuron level. We applied our framework to open-source LLMs such as Llama2 and Vicuna and had multiple interesting discoveries. Based on a layer-level causality analysis, we show that RLHF has the effect of overfitting a model to harmful prompts. It implies that such security can be easily overcome by `unusual' harmful prompts. As evidence, we propose an adversarial perturbation method that achieves 100\% attack success rate on the red-teaming tasks of the Trojan Detection Competition 2023. Furthermore, we show the existence of one mysterious neuron in both Llama2 and Vicuna that has an unreasonably high causal effect on the output. While we are uncertain on why such a neuron exists, we show that it is possible to conduct a ``Trojan'' attack targeting that particular neuron to completely cripple the LLM, i.e., we can generate transferable suffixes to prompts that frequently make the LLM produce meaningless responses.
The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and Guidelines
Venue: arXiv cs.HC 2023
Year: 2023

Pilot studies are an essential cornerstone of the design of crowdsourcing campaigns, yet they are often only mentioned in passing in the scholarly literature. A lack of details surrounding pilot studies in crowdsourcing research hinders the replication of studies and the reproduction of findings, stalling potential scientific advances. We conducted a systematic literature review on the current state of pilot study reporting at the intersection of crowdsourcing and HCI research. Our review of ten years of literature included 171 articles published in the proceedings of the Conference on Human Computation and Crowdsourcing (AAAI HCOMP) and the ACM Digital Library. We found that pilot studies in crowdsourcing research (i.e., crowd pilot studies) are often under-reported in the literature. Important details, such as the number of workers and rewards to workers, are often not reported. On the basis of our findings, we reflect on the current state of practice and formulate a set of best practice guidelines for reporting crowd pilot studies in crowdsourcing research. We also provide implications for the design of crowdsourcing platforms and make practical suggestions for supporting crowd pilot study reporting.
Adversarial Socialbots Modeling Based on Structural Information Principles
Venue: arXiv cs.SI 2023
Year: 2023

The importance of effective detection is underscored by the fact that socialbots imitate human behavior to propagate misinformation, leading to an ongoing competition between socialbots and detectors. Despite the rapid advancement of reactive detectors, the exploration of adversarial socialbot modeling remains incomplete, significantly hindering the development of proactive detectors. To address this issue, we propose a mathematical Structural Information principles-based Adversarial Socialbots Modeling framework, namely SIASM, to enable more accurate and effective modeling of adversarial behaviors. First, a heterogeneous graph is presented to integrate various users and rich activities in the original social network and measure its dynamic uncertainty as structural entropy. By minimizing the high-dimensional structural entropy, a hierarchical community structure of the social network is generated and referred to as the optimal encoding tree. Secondly, a novel method is designed to quantify influence by utilizing the assigned structural entropy, which helps reduce the computational cost of SIASM by filtering out uninfluential users. Besides, a new conditional structural entropy is defined between the socialbot and other users to guide the follower selection for network influence maximization. Extensive and comparative experiments on both homogeneous and heterogeneous social networks demonstrate that, compared with state-of-the-art baselines, the proposed SIASM framework yields substantial performance improvements in terms of network influence (up to 16.32%) and sustainable stealthiness (up to 16.29%) when evaluated against a robust detector with 90% accuracy.
Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation
Venue: arXiv cs.LG 2023
Year: 2023

The Bin Packing Problem (BPP) is a well-established combinatorial optimization (CO) problem. Since it has many applications in our daily life, e.g. logistics and resource allocation, people are seeking efficient bin packing algorithms. On the other hand, researchers have been making constant advances in machine learning (ML), which is famous for its efficiency. In this article, we first formulate BPP, introducing its variants and practical constraints. Then, a comprehensive survey on ML for multi-dimensional BPP is provided. We further collect some public benchmarks of 3D BPP, and evaluate some online methods on the Cutting Stock Dataset. Finally, we share our perspective on challenges and future directions in BPP. To the best of our knowledge, this is the first systematic review of ML-related methods for BPP.
Causal Optimal Transport of Abstractions
Venue: arXiv cs.LG 2023
Year: 2023

Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity by defining maps between them. These maps have significant relevance for real-world challenges such as synthesizing causal evidence from multiple experimental environments, learning causally consistent representations at different resolutions, and linking interventions across multiple SCMs. In this work, we propose COTA, the first method to learn abstraction maps from observational and interventional data without assuming complete knowledge of the underlying SCMs. In particular, we introduce a multi-marginal Optimal Transport (OT) formulation that enforces do-calculus causal constraints, together with a cost function that relies on interventional information. We extensively evaluate COTA on synthetic and real world problems, and showcase its advantages over non-causal, independent and aggregated COTA formulations. Finally, we demonstrate the efficiency of our method as a data augmentation tool by comparing it against the state-of-the-art CA learning framework, which assumes fully specified SCMs, on a real-world downstream task.
ASC: Adaptive Scale Feature Map Compression for Deep Neural Network
Venue: arXiv cs.CV 2023
Year: 2023

Deep-learning accelerators are increasingly in demand; however, their performance is constrained by the size of the feature map, leading to high bandwidth requirements and large buffer sizes. We propose an adaptive scale feature map compression technique leveraging the unique properties of the feature map. This technique adopts independent channel indexing given the weak channel correlation and utilizes a cubical-like block shape to benefit from strong local correlations. The method further optimizes compression using a switchable endpoint mode and adaptive scale interpolation to handle unimodal data distributions, both with and without outliers. This results in 4$\times$ and up to 7.69$\times$ compression rates for 16-bit data in constant and variable bitrates, respectively. Our hardware design minimizes area cost by adjusting interpolation scales, which facilitates hardware sharing among interpolation points. Additionally, we introduce a threshold concept for straightforward interpolation, preventing the need for intricate hardware. The TSMC 28nm implementation showcases an equivalent gate count of 6135 for the 8-bit version. Furthermore, the hardware architecture scales effectively, with only a sublinear increase in area cost. Achieving a 32$\times$ throughput increase meets the theoretical bandwidth of DDR5-6400 at just 7.65$\times$ the hardware cost.
Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-smoothness in Deep GNNs
Venue: arXiv cs.LG 2023
Year: 2023

Despite Graph neural networks' significant performance gain over many classic techniques in various graph-related downstream tasks, their successes are restricted in shallow models due to over-smoothness and the difficulties of optimizations among many other issues. In this paper, to alleviate the over-smoothing issue, we propose a soft graph normalization method to preserve the diversities of node embeddings and prevent indiscrimination due to possible over-closeness. Combined with residual connections, we analyze the reason why the method can effectively capture the knowledge in both input graph structures and node features even with deep networks. Additionally, inspired by Curriculum Learning that learns easy examples before the hard ones, we propose a novel label-smoothing-based learning framework to enhance the optimization of deep GNNs, which iteratively smooths labels in an auxiliary graph and constructs many gradual non-smooth tasks for extracting increasingly complex knowledge and gradually discriminating nodes from coarse to fine. The method arguably reduces the risk of overfitting and generalizes better results. Finally, extensive experiments are carried out to demonstrate the effectiveness and potential of the proposed model and learning framework through comparison with twelve existing baselines including the state-of-the-art methods on twelve real-world node classification benchmarks.
Combinatorial Stationary Prophet Inequalities
Venue: arXiv cs.GT 2023
Year: 2023

Numerous recent papers have studied the tension between thickening and clearing a market in (uncertain, online) long-time horizon Markovian settings. In particular, (Aouad and Sarita{ç} EC'20, Collina et al. WINE'20, Kessel et al. EC'22) studied what the latter referred to as the Stationary Prophet Inequality Problem, due to its similarity to the classic finite-time horizon prophet inequality problem. These works all consider unit-demand buyers. Mirroring the long line of work on the classic prophet inequality problem subject to combinatorial constraints, we initiate the study of the stationary prophet inequality problem subject to combinatorially-constrained buyers. Our results can be summarized succinctly as unearthing an algorithmic connection between contention resolution schemes (CRS) and stationary prophet inequalities. While the classic prophet inequality problem has a tight connection to online CRS (Feldman et al. SODA'16, Lee and Singla ESA'18), we show that for the stationary prophet inequality problem, offline CRS play a similarly central role. We show that, up to small constant factors, the best (ex-ante) competitive ratio achievable for the combinatorial prophet inequality equals the best possible balancedness achievable by offline CRS for the same combinatorial constraints.
On the verification of Embeddings using Hybrid Markov Logic
Venue: arXiv cs.LG 2023
Year: 2023

The standard approach to verify representations learned by Deep Neural Networks is to use them in specific tasks such as classification or regression, and measure their performance based on accuracy in such tasks. However, in many cases, we would want to verify more complex properties of a learned representation. To do this, we propose a framework based on a probabilistic first-order language, namely, Hybrid Markov Logic Networks (HMLNs) where we specify properties over embeddings mixed with symbolic domain knowledge. We present an approach to learn parameters for the properties within this framework. Further, we develop a verification method to test embeddings in this framework by encoding this task as a Mixed Integer Linear Program for which we can leverage existing state-of-the-art solvers. We illustrate verification in Graph Neural Networks, Deep Knowledge Tracing and Intelligent Tutoring Systems to demonstrate the generality of our approach.
FASTEN: Towards a FAult-tolerant and STorage EfficieNt Cloud: Balancing Between Replication and Deduplication
Venue: arXiv cs.DC 2023
Year: 2023

With the surge in cloud storage adoption, enterprises face challenges managing data duplication and exponential data growth. Deduplication mitigates redundancy, yet maintaining redundancy ensures high availability, incurring storage costs. Balancing these aspects is a significant research concern. We propose FASTEN, a distributed cloud storage scheme ensuring efficiency, security, and high availability. FASTEN achieves fault tolerance by dispersing data subsets optimally across servers and maintains redundancy for high availability. Experimental results show FASTEN's effectiveness in fault tolerance, cost reduction, batch auditing, and file and block-level deduplication. It outperforms existing systems with low time complexity, strong fault tolerance, and commendable deduplication performance.
View-Dependent Octree-based Mesh Extraction in Unbounded Scenes for Procedural Synthetic Data
Venue: arXiv cs.CV 2023
Year: 2023

Procedural synthetic data generation has received increasing attention in computer vision. Procedural signed distance functions (SDFs) are a powerful tool for modeling large-scale detailed scenes, but existing mesh extraction methods have artifacts or performance profiles that limit their use for synthetic data. We propose OcMesher, a mesh extraction algorithm that efficiently handles high-detail unbounded scenes with perfect view-consistency, with easy export to downstream real-time engines. The main novelty of our solution is an algorithm to construct an octree based on a given SDF and multiple camera views. We performed extensive experiments, and show our solution produces better synthetic data for training and evaluation of computer vision models.
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis
Venue: arXiv cs.LG 2023
Year: 2023

The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models. However, the neuroimaging field is notably hampered by the scarcity of such datasets. In this work, we proposed a data augmentation and validation framework that utilizes dynamic forecasting with Long Short-Term Memory (LSTM) networks to enrich datasets. We extended multivariate time series data by predicting the time courses of independent component networks (ICNs) in both one-step and recursive configurations. The effectiveness of these augmented datasets was then compared with the original data using various deep learning models designed for chronological age prediction tasks. The results suggest that our approach improves model performance, providing a robust solution to overcome the challenges presented by the limited size of neuroimaging datasets.
Exploring Graph Based Approaches for Author Name Disambiguation
Venue: arXiv cs.SI 2023
Year: 2023

In many applications, such as scientific literature management, researcher search, social network analysis and etc, Name Disambiguation (aiming at disambiguating WhoIsWho) has been a challenging problem. In addition, the growth of scientific literature makes the problem more difficult and urgent. Although name disambiguation has been extensively studied in academia and industry, the problem has not been solved well due to the clutter of data and the complexity of the same name scenario. In this work, we aim to explore models that can perform the task of name disambiguation using the network structure that is intrinsic to the problem and present an analysis of the models.
Accelerating Meta-Learning by Sharing Gradients
Venue: arXiv cs.LG 2023
Year: 2023

The success of gradient-based meta-learning is primarily attributed to its ability to leverage related tasks to learn task-invariant information. However, the absence of interactions between different tasks in the inner loop leads to task-specific over-fitting in the initial phase of meta-training. While this is eventually corrected by the presence of these interactions in the outer loop, it comes at a significant cost of slower meta-learning. To address this limitation, we explicitly encode task relatedness via an inner loop regularization mechanism inspired by multi-task learning. Our algorithm shares gradient information from previously encountered tasks as well as concurrent tasks in the same task batch, and scales their contribution with meta-learned parameters. We show using two popular few-shot classification datasets that gradient sharing enables meta-learning under bigger inner loop learning rates and can accelerate the meta-training process by up to 134%.
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability
Venue: arXiv cs.LG 2023
Year: 2023

Grasslands are known for their high biodiversity and ability to provide multiple ecosystem services. Challenges in automating the identification of indicator plants are key obstacles to large-scale grassland monitoring. These challenges stem from the scarcity of extensive datasets, the distributional shifts between generic and grassland-specific datasets, and the inherent opacity of deep learning models. This paper delves into the latter two challenges, with a specific focus on transfer learning and eXplainable Artificial Intelligence (XAI) approaches to grassland monitoring, highlighting the novelty of XAI in this domain. We analyze various transfer learning methods to bridge the distributional gaps between generic and grassland-specific datasets. Additionally, we showcase how explainable AI techniques can unveil the model's domain adaptation capabilities, employing quantitative assessments to evaluate the model's proficiency in accurately centering relevant input features around the object of interest. This research contributes valuable insights for enhancing model performance through transfer learning and measuring domain adaptability with explainable AI, showing significant promise for broader applications within the agricultural community.
Privacy Constrained Fairness Estimation for Decision Trees
Venue: arXiv cs.LG 2023
Year: 2023

The protection of sensitive data becomes more vital, as data increases in value and potency. Furthermore, the pressure increases from regulators and society on model developers to make their Artificial Intelligence (AI) models non-discriminatory. To boot, there is a need for interpretable, transparent AI models for high-stakes tasks. In general, measuring the fairness of any AI model requires the sensitive attributes of the individuals in the dataset, thus raising privacy concerns. In this work, the trade-offs between fairness, privacy and interpretability are further explored. We specifically examine the Statistical Parity (SP) of Decision Trees (DTs) with Differential Privacy (DP), that are each popular methods in their respective subfield. We propose a novel method, dubbed Privacy-Aware Fairness Estimation of Rules (PAFER), that can estimate SP in a DP-aware manner for DTs. DP, making use of a third-party legal entity that securely holds this sensitive data, guarantees privacy by adding noise to the sensitive data. We experimentally compare several DP mechanisms. We show that using the Laplacian mechanism, the method is able to estimate SP with low error while guaranteeing the privacy of the individuals in the dataset with high certainty. We further show experimentally and theoretically that the method performs better for DTs that humans generally find easier to interpret.
E&V: Prompting Large Language Models to Perform Static Analysis by Pseudo-code Execution and Verification
Venue: arXiv cs.SE 2023
Year: 2023

Static analysis, the process of examining code without executing it, is crucial for identifying software issues. Yet, static analysis is hampered by its complexity and the need for customization for different targets. Traditional static analysis tools require extensive human effort and are often limited to specific target programs and programming languages. Recent advancements in Large Language Models (LLMs), such as GPT-4 and Llama, offer new capabilities for software engineering tasks. However, their application in static analysis, especially in understanding complex code structures, remains under-explored. This paper introduces a novel approach named E&V , which leverages LLMs to perform static analysis. Specifically, E&V employs LLMs to simulate the execution of pseudo-code, effectively conducting static analysis encoded in the pseudo-code with minimal human effort, thereby improving the accuracy of results. E&V includes a verification process for pseudo-code execution without needing an external oracle. This process allows E&V to mitigate hallucinations of LLMs and enhance the accuracy of static analysis results. We have implemented E&V in a prototype tool designed for triaging crashes through backward taint analysis. This prototype, paired with GPT-4-32k, has been applied to triage 170 recently fixed Linux kernel bugs across seven bug categories. Our experiments demonstrate that the prototype correctly identifies the blamed function in 81.2% of the cases. Additionally, we observe that our novel verification process significantly improves the accuracy, increasing it from 28.2% to 81.2%.
PerMod: Perceptually Grounded Voice Modification with Latent Diffusion Models
Venue: arXiv cs.SD 2023
Year: 2023

Perceptual modification of voice is an elusive goal. While non-experts can modify an image or sentence perceptually with available tools, it is not clear how to similarly modify speech along perceptual axes. Voice conversion does make it possible to convert one voice to another, but these modifications are handled by black box models, and the specifics of what perceptual qualities to modify and how to modify them are unclear. Towards allowing greater perceptual control over voice, we introduce PerMod, a conditional latent diffusion model that takes in an input voice and a perceptual qualities vector, and produces a voice with the matching perceptual qualities. Unlike prior work, PerMod generates a new voice corresponding to specific perceptual modifications. Evaluating perceptual quality vectors with RMSE from both human and predicted labels, we demonstrate that PerMod produces voices with the desired perceptual qualities for typical voices, but performs poorly on atypical voices.
Contractive error feedback for gradient compression
Venue: arXiv cs.LG 2023
Year: 2023

On-device memory concerns in distributed deep learning have become severe due to (i) the growth of model size in multi-GPU training, and (ii) the wide adoption of deep neural networks for federated learning on IoT devices which have limited storage. In such settings, communication efficient optimization methods are attractive alternatives, however they still struggle with memory issues. To tackle these challenges, we propose an communication efficient method called contractive error feedback (ConEF). As opposed to SGD with error-feedback (EFSGD) that inefficiently manages memory, ConEF obtains the sweet spot of convergence and memory usage, and achieves communication efficiency by leveraging biased and all-reducable gradient compression. We empirically validate ConEF on various learning tasks that include image classification, language modeling, and machine translation and observe that ConEF saves 80\% - 90\% of the extra memory in EFSGD with almost no loss on test performance, while also achieving 1.3x - 5x speedup of SGD. Through our work, we also demonstrate the feasibility and convergence of ConEF to clear up the theoretical barrier of integrating ConEF to popular memory efficient frameworks such as ZeRO-3.