OpenRaiser/Intern-Atlas huggingface dataset:
Views Are My Own, but Also Yours: Benchmarking Theory of Mind Using Common Ground
Venue: arXiv cs.CL 2024
Year: 2024

Evaluating the theory of mind (ToM) capabilities of language models (LMs) has recently received a great deal of attention. However, many existing benchmarks rely on synthetic data, which risks misaligning the resulting experiments with human behavior. We introduce the first ToM dataset based on naturally occurring spoken dialogs, Common-ToM, and show that LMs struggle to demonstrate ToM. We then show that integrating a simple, explicit representation of beliefs improves LM performance on Common-ToM.
OffensiveLang: A Community Based Implicit Offensive Language Dataset
Venue: arXiv cs.CL 2024
Year: 2024

The widespread presence of hateful languages on social media has resulted in adverse effects on societal well-being. As a result, addressing this issue with high priority has become very important. Hate speech or offensive languages exist in both explicit and implicit forms, with the latter being more challenging to detect. Current research in this domain encounters several challenges. Firstly, the existing datasets primarily rely on the collection of texts containing explicit offensive keywords, making it challenging to capture implicitly offensive contents that are devoid of these keywords. Secondly, common methodologies tend to focus solely on textual analysis, neglecting the valuable insights that community information can provide. In this research paper, we introduce a novel dataset OffensiveLang, a community based implicit offensive language dataset generated by ChatGPT 3.5 containing data for 38 different target groups. Despite limitations in generating offensive texts using ChatGPT due to ethical constraints, we present a prompt-based approach that effectively generates implicit offensive languages. To ensure data quality, we evaluate the dataset with human. Additionally, we employ a prompt-based zero-shot method with ChatGPT and compare the detection results between human annotation and ChatGPT annotation. We utilize existing state-of-the-art models to see how effective they are in detecting such languages. The dataset is available here: https://github.com/AmitDasRup123/OffensiveLang
The complexity of computing in continuous time: space complexity is precision
Venue: arXiv cs.CC 2024
Year: 2024

Models of computations over the integers are equivalent from a computability and complexity theory point of view by the Church-Turing thesis. It is not possible to unify discrete-time models over the reals. The situation is unclear but simpler for continuous-time models, as there is a unifying mathematical model provided by ordinary differential equations (ODEs). For example, the GPAC model of Shannon is known to correspond to polynomial ODEs. However, the question of a robust complexity theory for such models and its relations to classical (discrete) computation theory is an old problem. There was some recent significant progress: it has been proved that (classical) time complexity corresponds to the length of the involved curves. The question of whether there is a simple and robust way to measure space complexity remains. We argue that space complexity corresponds to precision and conversely. We propose and prove an algebraic characterisation of FPSPACE, using continuous ODEs. Recent papers proposed algebraic characterisations of polynomial-time and -space complexity classes over the reals, but with a discrete-time: those algebras rely on discrete ODE schemes. Here, we use classical (continuous) ODEs, with the classic definition of derivation and hence with the more natural context of continuous-time associated with ODEs. We characterise both the case of polynomial space functions over the integers and the reals. We prove that Turing machines, with a proper representation of real numbers, can be simulated by continuous ODEs and not just discrete ODEs. A major consequence is that the associated space complexity is provably related to the numerical stability of involved schemas and the associated required precision. We obtain that a problem can be solved in polynomial space if and only if it can be simulated by some numerically stable ODE, using a polynomial precision.
A Tutorial on the Pretrain-Finetune Paradigm for Natural Language Processing
Venue: arXiv cs.CL 2024
Year: 2024

Given that natural language serves as the primary conduit for expressing thoughts and emotions, text analysis has become a key technique in psychological research. It enables the extraction of valuable insights from natural language, facilitating endeavors like personality traits assessment, mental health monitoring, and sentiment analysis in interpersonal communications. In text analysis, existing studies often resort to either human coding, which is time-consuming, using pre-built dictionaries, which often fails to cover all possible scenarios, or training models from scratch, which requires large amounts of labeled data. In this tutorial, we introduce the pretrain-finetune paradigm. The pretrain-finetune paradigm represents a transformative approach in text analysis and natural language processing. This paradigm distinguishes itself through the use of large pretrained language models, demonstrating remarkable efficiency in finetuning tasks, even with limited training data. This efficiency is especially beneficial for research in social sciences, where the number of annotated samples is often quite limited. Our tutorial offers a comprehensive introduction to the pretrain-finetune paradigm. We first delve into the fundamental concepts of pretraining and finetuning, followed by practical exercises using real-world applications. We demonstrate the application of the paradigm across various tasks, including multi-class classification and regression. Emphasizing its efficacy and user-friendliness, the tutorial aims to encourage broader adoption of this paradigm. To this end, we have provided open access to all our code and datasets. The tutorial is highly beneficial across various psychology disciplines, providing a comprehensive guide to employing text analysis in diverse research settings.
SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

In recent years, large language models (LLMs) have become increasingly prevalent, offering remarkable text generation capabilities. However, a pressing challenge is their tendency to make confidently wrong predictions, highlighting the critical need for uncertainty quantification (UQ) in LLMs. While previous works have mainly focused on addressing aleatoric uncertainty, the full spectrum of uncertainties, including epistemic, remains inadequately explored. Motivated by this gap, we introduce a novel UQ method, sampling with perturbation for UQ (SPUQ), designed to tackle both aleatoric and epistemic uncertainties. The method entails generating a set of perturbations for LLM inputs, sampling outputs for each perturbation, and incorporating an aggregation module that generalizes the sampling uncertainty approach for text generation tasks. Through extensive experiments on various datasets, we investigated different perturbation and aggregation techniques. Our findings show a substantial improvement in model uncertainty calibration, with a reduction in Expected Calibration Error (ECE) by 50\% on average. Our findings suggest that our proposed UQ method offers promising steps toward enhancing the reliability and trustworthiness of LLMs.
MPI Errors Detection using GNN Embedding and Vector Embedding over LLVM IR
Venue: arXiv cs.DC 2024
Year: 2024

Identifying errors in parallel MPI programs is a challenging task. Despite the growing number of verification tools, debugging parallel programs remains a significant challenge. This paper is the first to utilize embedding and deep learning graph neural networks (GNNs) to tackle the issue of identifying bugs in MPI programs. Specifically, we have designed and developed two models that can determine, from a code's LLVM Intermediate Representation (IR), whether the code is correct or contains a known MPI error. We tested our models using two dedicated MPI benchmark suites for verification: MBI and MPI-CorrBench. By training and validating our models on the same benchmark suite, we achieved a prediction accuracy of 92% in detecting error types. Additionally, we trained and evaluated our models on distinct benchmark suites (e.g., transitioning from MBI to MPI-CorrBench) and achieved a promising accuracy of over 80%. Finally, we investigated the interaction between different MPI errors and quantified our models' generalization capabilities over new unseen errors. This involved removing error types during training and assessing whether our models could still predict them. The detection accuracy of removed errors varies significantly between 20% to 80%, indicating connected error patterns.
Catch'em all: Classification of Rare, Prominent, and Novel Malware Families
Venue: arXiv cs.CR 2024
Year: 2024

National security is threatened by malware, which remains one of the most dangerous and costly cyber threats. As of last year, researchers reported 1.3 billion known malware specimens, motivating the use of data-driven machine learning (ML) methods for analysis. However, shortcomings in existing ML approaches hinder their mass adoption. These challenges include detection of novel malware and the ability to perform malware classification in the face of class imbalance: a situation where malware families are not equally represented in the data. Our work addresses these shortcomings with MalwareDNA: an advanced dimensionality reduction and feature extraction framework. We demonstrate stable task performance under class imbalance for the following tasks: malware family classification and novel malware detection with a trade-off in increased abstention or reject-option rate.
Designing Born-Accessible Courses in Data Science and Visualization: Challenges and Opportunities of a Remote Curriculum Taught by Blind Instructors to Blind Students
Venue: arXiv cs.HC 2024
Year: 2024

While recent years have seen a growing interest in accessible visualization tools and techniques for blind people, little attention is paid to the learning opportunities and teaching strategies of data science and visualization tailored for blind individuals. Whereas the former focuses on the accessibility issues of data visualization tools, the latter is concerned with the learnability of concepts and skills for data science and visualization. In this paper, we present novel approaches to teaching data science and visualization to blind students in an online setting. Taught by blind instructors, nine blind learners having a wide range of professional backgrounds participated in a two-week summer course. We describe the course design, teaching strategies, and learning outcomes. We also discuss the challenges and opportunities of teaching data science and visualization to blind students. Our work contributes to the growing body of knowledge on accessible data science and visualization education, and provides insights into the design of online courses for blind students.
DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training
Venue: arXiv cs.LG 2024
Year: 2024

Recent developments have underscored the critical role of \textit{differential privacy} (DP) in safeguarding individual data for training machine learning models. However, integrating DP oftentimes incurs significant model performance degradation due to the perturbation introduced into the training process, presenting a formidable challenge in the {differentially private machine learning} (DPML) field. To this end, several mitigative efforts have been proposed, typically revolving around formulating new DPML algorithms or relaxing DP definitions to harmonize with distinct contexts. In spite of these initiatives, the diminishment induced by DP on models, particularly large-scale models, remains substantial and thus, necessitates an innovative solution that adeptly circumnavigates the consequential impairment of model utility. In response, we introduce DPAdapter, a pioneering technique designed to amplify the model performance of DPML algorithms by enhancing parameter robustness. The fundamental intuition behind this strategy is that models with robust parameters are inherently more resistant to the noise introduced by DP, thereby retaining better performance despite the perturbations. DPAdapter modifies and enhances the sharpness-aware minimization (SAM) technique, utilizing a two-batch strategy to provide a more accurate perturbation estimate and an efficient gradient descent, thereby improving parameter robustness against noise. Notably, DPAdapter can act as a plug-and-play component and be combined with existing DPML algorithms to further improve their performance. Our experiments show that DPAdapter vastly enhances state-of-the-art DPML algorithms, increasing average accuracy from 72.92\% to 77.09\% with a privacy budget of $ε=4$.
ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary
Venue: arXiv cs.IR 2024
Year: 2024

The literature review is an indispensable step in the research process. It provides the benefit of comprehending the research problem and understanding the current research situation while conducting a comparative analysis of prior works. However, literature summary is challenging and time consuming. The previous LLM-based studies on literature review mainly focused on the complete process, including literature retrieval, screening, and summarization. However, for the summarization step, simple CoT method often lacks the ability to provide extensive comparative summary. In this work, we firstly focus on the independent literature summarization step and introduce ChatCite, an LLM agent with human workflow guidance for comparative literature summary. This agent, by mimicking the human workflow, first extracts key elements from relevant literature and then generates summaries using a Reflective Incremental Mechanism. In order to better evaluate the quality of the generated summaries, we devised a LLM-based automatic evaluation metric, G-Score, in refer to the human evaluation criteria. The ChatCite agent outperformed other models in various dimensions in the experiments. The literature summaries generated by ChatCite can also be directly used for drafting literature reviews.
AceMap: Knowledge Discovery through Academic Graph
Venue: arXiv cs.DL 2024
Year: 2024

The exponential growth of scientific literature requires effective management and extraction of valuable insights. While existing scientific search engines excel at delivering search results based on relational databases, they often neglect the analysis of collaborations between scientific entities and the evolution of ideas, as well as the in-depth analysis of content within scientific publications. The representation of heterogeneous graphs and the effective measurement, analysis, and mining of such graphs pose significant challenges. To address these challenges, we present AceMap, an academic system designed for knowledge discovery through academic graph. We present advanced database construction techniques to build the comprehensive AceMap database with large-scale academic entities that contain rich visual, textual, and numerical information. AceMap also employs innovative visualization, quantification, and analysis methods to explore associations and logical relationships among academic entities. AceMap introduces large-scale academic network visualization techniques centered on nebular graphs, providing a comprehensive view of academic networks from multiple perspectives. In addition, AceMap proposes a unified metric based on structural entropy to quantitatively measure the knowledge content of different academic entities. Moreover, AceMap provides advanced analysis capabilities, including tracing the evolution of academic ideas through citation relationships and concept co-occurrence, and generating concise summaries informed by this evolutionary process. In addition, AceMap uses machine reading methods to generate potential new ideas at the intersection of different fields. Exploring the integration of large language models and knowledge graphs is a promising direction for future research in idea evolution. Please visit \url{https://www.acemap.info} for further exploration.
DNNLasso: Scalable Graph Learning for Matrix-Variate Data
Venue: arXiv cs.LG 2024
Year: 2024

We consider the problem of jointly learning row-wise and column-wise dependencies of matrix-variate observations, which are modelled separately by two precision matrices. Due to the complicated structure of Kronecker-product precision matrices in the commonly used matrix-variate Gaussian graphical models, a sparser Kronecker-sum structure was proposed recently based on the Cartesian product of graphs. However, existing methods for estimating Kronecker-sum structured precision matrices do not scale well to large scale datasets. In this paper, we introduce DNNLasso, a diagonally non-negative graphical lasso model for estimating the Kronecker-sum structured precision matrix, which outperforms the state-of-the-art methods by a large margin in both accuracy and computational time. Our code is available at https://github.com/YangjingZhang/DNNLasso.
PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of Multi-Agent Reinforcement Learning
Venue: arXiv cs.AI 2024
Year: 2024

Training for multi-agent reinforcement learning(MARL) is a time-consuming process caused by distribution shift of each agent. One drawback is that strategy of each agent in MARL is independent but actually in cooperation. Thus, a vertical issue in multi-agent reinforcement learning is how to efficiently accelerate training process. To address this problem, current research has leveraged a centralized function(CF) across multiple agents to learn contribution of the team reward for each agent. However, CF based methods introduce joint error from other agents in estimation of value network. In so doing, inspired by federated learning, we propose three simple novel approaches called Average Periodically Parameter Sharing(A-PPS), Reward-Scalability Periodically Parameter Sharing(RS-PPS) and Partial Personalized Periodically Parameter Sharing(PP-PPS) mechanism to accelerate training of MARL. Agents share Q-value network periodically during the training process. Agents which has same identity adapt collected reward as scalability and update partial neural network during period to share different parameters. We apply our approaches in classical MARL method QMIX and evaluate our approaches on various tasks in StarCraft Multi-Agent Challenge(SMAC) environment. Performance of numerical experiments yield enormous enhancement, with an average improvement of 10\%-30\%, and enable to win tasks that QMIX cannot. Our code can be downloaded from https://github.com/ColaZhang22/PPS-QMIX
Algorithms for Galois Words: Detection, Factorization, and Rotation
Venue: arXiv cs.DS 2024
Year: 2024

Lyndon words are extensively studied in combinatorics on words -- they play a crucial role on upper bounding the number of runs a word can have [Bannai+, SIAM J. Comput.'17]. We can determine Lyndon words, factorize a word into Lyndon words in lexicographically non-increasing order, and find the Lyndon rotation of a word, all in linear time within constant additional working space. A recent research interest emerged from the question of what happens when we change the lexicographic order, which is at the heart of the definition of Lyndon words. In particular, the alternating order, where the order of all odd positions becomes reversed, has been recently proposed. While a Lyndon word is, among all its cyclic rotations, the smallest one with respect to the lexicographic order, a Galois word exhibits the same property by exchanging the lexicographic order with the alternating order. Unfortunately, this exchange has a large impact on the properties Galois words exhibit, which makes it a nontrivial task to translate results from Lyndon words to Galois words. Up until now, it has only been conjectured that linear-time algorithms with constant additional working space in the spirit of Duval's algorithm are possible for computing the Galois factorization or the Galois rotation. Here, we affirm this conjecture as follows. Given a word $T$ of length $n$, we can determine whether $T$ is a Galois word, in $O(n)$ time with constant additional working space. Within the same complexities, we can also determine the Galois rotation of $T$, and compute the Galois factorization of $T$ online. The last result settles Open Problem~1 in [Dolce et al., TCS 2019] for Galois words.
UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation
Venue: arXiv cs.RO 2024
Year: 2024

Autonomous off-road navigation requires an accurate semantic understanding of the environment, often converted into a bird's-eye view (BEV) representation for various downstream tasks. While learning-based methods have shown success in generating local semantic terrain maps directly from sensor data, their efficacy in off-road environments is hindered by challenges in accurately representing uncertain terrain features. This paper presents a learning-based fusion method for generating dense terrain classification maps in BEV. By performing LiDAR-image fusion at multiple scales, our approach enhances the accuracy of semantic maps generated from an RGB image and a single-sweep LiDAR scan. Utilizing uncertainty-aware pseudo-labels further enhances the network's ability to learn reliably in off-road environments without requiring precise 3D annotations. By conducting thorough experiments using off-road driving datasets, we demonstrate that our method can improve accuracy in off-road terrains, validating its efficacy in facilitating reliable and safe autonomous navigation in challenging off-road settings.
Optimizing Mobile-Friendly Viewport Prediction for Live 360-Degree Video Streaming
Venue: arXiv cs.MM 2024
Year: 2024

Viewport prediction is the crucial task for adaptive 360-degree video streaming, as the bitrate control algorithms usually require the knowledge of the user's viewing portions of the frames. Various methods are studied and adopted for viewport prediction from less accurate statistic tools to highly calibrated deep neural networks. Conventionally, it is difficult to implement sophisticated deep learning methods on mobile devices, which have limited computation capability. In this work, we propose an advanced learning-based viewport prediction approach and carefully design it to introduce minimal transmission and computation overhead for mobile terminals. We also propose a model-agnostic meta-learning (MAML) based saliency prediction network trainer, which provides a few-sample fast training solution to obtain the prediction model by utilizing the information from the past models. We further discuss how to integrate this mobile-friendly viewport prediction (MFVP) approach into a typical 360-degree video live streaming system by formulating and solving the bitrate adaptation problem. Extensive experiment results show that our prediction approach can work in real-time for live video streaming and can achieve higher accuracies compared to other existing prediction methods on mobile end, which, together with our bitrate adaptation algorithm, significantly improves the streaming QoE from various aspects. We observe the accuracy of MFVP is 8.1$\%$ to 28.7$\%$ higher than other algorithms and achieves 3.73$\%$ to 14.96$\%$ higher average quality level and 49.6$\%$ to 74.97$\%$ less quality level change than other algorithms.