pwc-archive/papers-with-abstracts huggingface dataset:
Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamics
Authors: Weitong Zhang, Chengqi Zang, Liu Li, Sarah Cechnicka, Cheng Ouyang, Bernhard Kainz
Date: 2024-06-19 00:00:00

Inverse problems describe the process of estimating the causal factors from a set of measurements or data. Mapping of often incomplete or degraded data to parameters is ill-posed, thus data-driven iterative solutions are required, for example when reconstructing clean images from poor signals. Diffusion models have shown promise as potent generative tools for solving inverse problems due to their superior reconstruction quality and their compatibility with iterative solvers. However, most existing approaches are limited to linear inverse problems represented as Stochastic Differential Equations (SDEs). This simplification falls short of addressing the challenging nature of real-world problems, leading to amplified cumulative errors and biases. We provide an explanation for this gap through the lens of measure-preserving dynamics of Random Dynamical Systems (RDS) with which we analyse Temporal Distribution Discrepancy and thus introduce a theoretical framework based on RDS for SDE diffusion models. We uncover several strategies that inherently enhance the stability and generalizability of diffusion models for inverse problems and introduce a novel score-based diffusion framework, the \textbf{D}ynamics-aware S\textbf{D}E \textbf{D}iffusion \textbf{G}enerative \textbf{M}odel (D$^3$GM). The \textit{Measure-preserving property} can return the degraded measurement to the original state despite complex degradation with the RDS concept of \textit{stability}. Our extensive experimental results corroborate the effectiveness of D$^3$GM across multiple benchmarks including a prominent application for inverse problems, magnetic resonance imaging. Code and data will be publicly available.
On AI-Inspired UI-Design
Authors: Jialiang Wei, Anne-Lise Courbis, Thomas Lambolais, Gérard Dray, Walid Maalej
Date: 2024-06-19 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Graphical User Interface (or simply UI) is a primary mean of interaction between users and their devices. In this paper, we discuss three complementary Artificial Intelligence (AI) approaches for triggering the creativity of app designers and inspiring them create better and more diverse UI designs. First, designers can prompt a Large Language Model (LLM) to directly generate and adjust UIs. Second, a Vision-Language Model (VLM) enables designers to effectively search a large screenshot dataset, e.g. from apps published in app stores. Third, a Diffusion Model (DM) can be trained to specifically generate UIs as inspirational images. We present an AI-inspired design process and discuss the implications and limitations of the approaches.
Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines
Authors: Kangtong Mo, Wenyan Liu, Xuanzhen Xu, Chang Yu, Yuelin Zou, Fangqing Xia
Date: 2024-06-19 00:00:00
Tasks: Management, Sentiment Analysis, Sentiment Classification

In this study, we explore the application of sentiment analysis on financial news headlines to understand investor sentiment. By leveraging Natural Language Processing (NLP) and Large Language Models (LLM), we analyze sentiment from the perspective of retail investors. The FinancialPhraseBank dataset, which contains categorized sentiments of financial news headlines, serves as the basis for our analysis. We fine-tuned several models, including distilbert-base-uncased, Llama, and gemma-7b, to evaluate their effectiveness in sentiment classification. Our experiments demonstrate that the fine-tuned gemma-7b model outperforms others, achieving the highest precision, recall, and F1 score. Specifically, the gemma-7b model showed significant improvements in accuracy after fine-tuning, indicating its robustness in capturing the nuances of financial sentiment. This model can be instrumental in providing market insights, risk management, and aiding investment decisions by accurately predicting the sentiment of financial news. The results highlight the potential of advanced LLMs in transforming how we analyze and interpret financial information, offering a powerful tool for stakeholders in the financial industry.
Enhance the Image: Super Resolution using Artificial Intelligence in MRI
Authors: Ziyu Li, Zihan Li, Haoxiang Li, Qiuyun Fan, Karla L. Miller, Wenchuan Wu, Akshay S. Chaudhari, Qiyuan Tian
Date: 2024-06-19 00:00:00
Tasks: Deep Learning, Image Super-Resolution, Super-Resolution

This chapter provides an overview of deep learning techniques for improving the spatial resolution of MRI, ranging from convolutional neural networks, generative adversarial networks, to more advanced models including transformers, diffusion models, and implicit neural representations. Our exploration extends beyond the methodologies to scrutinize the impact of super-resolved images on clinical and neuroscientific assessments. We also cover various practical topics such as network architectures, image evaluation metrics, network loss functions, and training data specifics, including downsampling methods for simulating low-resolution images and dataset selection. Finally, we discuss existing challenges and potential future directions regarding the feasibility and reliability of deep learning-based MRI super-resolution, with the aim to facilitate its wider adoption to benefit various clinical and neuroscientific applications.
Optimizing Psychological Counseling with Instruction-Tuned Large Language Models
Authors: Wenjie Li, Tianyu Sun, Kun Qian, Wenhong Wang
Date: 2024-06-19 00:00:00

The advent of large language models (LLMs) has significantly advanced various fields, including natural language processing and automated dialogue systems. This paper explores the application of LLMs in psychological counseling, addressing the increasing demand for mental health services. We present a method for instruction tuning LLMs with specialized prompts to enhance their performance in providing empathetic, relevant, and supportive responses. Our approach involves developing a comprehensive dataset of counseling-specific prompts, refining them through feedback from professional counselors, and conducting rigorous evaluations using both automatic metrics and human assessments. The results demonstrate that our instruction-tuned model outperforms several baseline LLMs, highlighting its potential as a scalable and accessible tool for mental health support.
Nicer Than Humans: How do Large Language Models Behave in the Prisoner's Dilemma?
Authors: Nicoló Fontana, Francesco Pierri, Luca Maria Aiello
Date: 2024-06-19 00:00:00
Tasks: Decision Making

The behavior of Large Language Models (LLMs) as artificial social agents is largely unexplored, and we still lack extensive evidence of how these agents react to simple social stimuli. Testing the behavior of AI agents in classic Game Theory experiments provides a promising theoretical framework for evaluating the norms and values of these agents in archetypal social situations. In this work, we investigate the cooperative behavior of three LLMs (Llama2, Llama3, and GPT3.5) when playing the Iterated Prisoner's Dilemma against random adversaries displaying various levels of hostility. We introduce a systematic methodology to evaluate an LLM's comprehension of the game rules and its capability to parse historical gameplay logs for decision-making. We conducted simulations of games lasting for 100 rounds and analyzed the LLMs' decisions in terms of dimensions defined in the behavioral economics literature. We find that all models tend not to initiate defection but act cautiously, favoring cooperation over defection only when the opponent's defection rate is low. Overall, LLMs behave at least as cooperatively as the typical human player, although our results indicate some substantial differences among models. In particular, Llama2 and GPT3.5 are more cooperative than humans, and especially forgiving and non-retaliatory for opponent defection rates below 30%. More similar to humans, Llama3 exhibits consistently uncooperative and exploitative behavior unless the opponent always cooperates. Our systematic approach to the study of LLMs in game theoretical scenarios is a step towards using these simulations to inform practices of LLM auditing and alignment.
Root Cause Localization for Microservice Systems in Cloud-edge Collaborative Environments
Authors: Yuhan Zhu, Jian Wang, Bing Li, Xuxian Tang, Hao Li, Neng Zhang, Yuqi Zhao
Date: 2024-06-19 00:00:00
Tasks: Graph Neural Network

With the development of cloud-native technologies, microservice-based software systems face challenges in accurately localizing root causes when failures occur. Additionally, the cloud-edge collaborative environment introduces more difficulties, such as unstable networks and high latency across network segments. Accurately identifying the root cause of microservices in a cloud-edge collaborative environment has thus become an urgent problem. In this paper, we propose MicroCERCL, a novel approach that pinpoints root causes at the kernel and application level in the cloud-edge collaborative environment. Our key insight is that failures propagate through direct invocations and indirect resource-competition dependencies in a cloud-edge collaborative environment characterized by instability and high latency. This will become more complex in the hybrid deployment that simultaneously involves multiple microservice systems. Leveraging this insight, we extract valid contents from kernel-level logs to prioritize localizing the kernel-level root cause. Moreover, we construct a heterogeneous dynamic topology stack and train a graph neural network model to accurately localize the application-level root cause without relying on historical data. Notably, we released the first benchmark hybrid deployment microservice system in a cloud-edge collaborative environment (the largest and most complex within our knowledge). Experiments conducted on the dataset collected from the benchmark show that MicroCERCL can accurately localize the root cause of microservice systems in such environments, significantly outperforming state-of-the-art approaches with an increase of at least 24.1% in top-1 accuracy.
CoDreamer: Communication-Based Decentralised World Models
Authors: Edan Toledo, Amanda Prorok
Date: 2024-06-19 00:00:00

Sample efficiency is a critical challenge in reinforcement learning. Model-based RL has emerged as a solution, but its application has largely been confined to single-agent scenarios. In this work, we introduce CoDreamer, an extension of the Dreamer algorithm for multi-agent environments. CoDreamer leverages Graph Neural Networks for a two-level communication system to tackle challenges such as partial observability and inter-agent cooperation. Communication is separately utilised within the learned world models and within the learned policies of each agent to enhance modelling and task-solving. We show that CoDreamer offers greater expressive power than a naive application of Dreamer, and we demonstrate its superiority over baseline methods across various multi-agent environments.
GraphKAN: Enhancing Feature Extraction with Graph Kolmogorov Arnold Networks
Authors: Fan Zhang, Xin Zhang
Date: 2024-06-19 00:00:00
Tasks: Kolmogorov-Arnold Networks

Massive number of applications involve data with underlying relationships embedded in non-Euclidean space. Graph neural networks (GNNs) are utilized to extract features by capturing the dependencies within graphs. Despite groundbreaking performances, we argue that Multi-layer perceptrons (MLPs) and fixed activation functions impede the feature extraction due to information loss. Inspired by Kolmogorov Arnold Networks (KANs), we make the first attempt to GNNs with KANs. We discard MLPs and activation functions, and instead used KANs for feature extraction. Experiments demonstrate the effectiveness of GraphKAN, emphasizing the potential of KANs as a powerful tool. Code is available at https://github.com/Ryanfzhang/GraphKan.
Submodular Participatory Budgeting
Authors: Jing Yuan, Shaojie Tang
Date: 2024-06-19 00:00:00

Participatory budgeting refers to the practice of allocating public resources by collecting and aggregating individual preferences. Most existing studies in this field often assume an additive utility function, where each individual holds a private utility for each candidate project, and the total utility of a set of funded projects is simply the sum of the utilities of all projects. We argue that this assumption does not always hold in reality. For example, building two playgrounds in the same neighborhood does not necessarily lead to twice the utility of building a single playground. To address this, we extend the existing study by proposing a submodular participatory budgeting problem, assuming that the utility function of each individual is a monotone and submodular function over funded projects. We propose and examine three preference elicitation methods, including \emph{ranking-by-marginal-values}, \emph{ranking-by-values} and \emph{threshold approval votes}, and analyze their performances in terms of distortion. Notably, if the utility function is addicative, our aggregation rule designed for threshold approval votes achieves a better distortion than the state-of-the-art approach.
Bayes' capacity as a measure for reconstruction attacks in federated learning
Authors: Sayan Biswas, Mark Dras, Pedro Faustini, Natasha Fernandes, Annabelle McIver, Catuscia Palamidessi, Parastoo Sadeghi
Date: 2024-06-19 00:00:00
Tasks: Federated Learning, Reconstruction Attack

Within the machine learning community, reconstruction attacks are a principal attack of concern and have been identified even in federated learning, which was designed with privacy preservation in mind. In federated learning, it has been shown that an adversary with knowledge of the machine learning architecture is able to infer the exact value of a training element given an observation of the weight updates performed during stochastic gradient descent. In response to these threats, the privacy community recommends the use of differential privacy in the stochastic gradient descent algorithm, termed DP-SGD. However, DP has not yet been formally established as an effective countermeasure against reconstruction attacks. In this paper, we formalise the reconstruction threat model using the information-theoretic framework of quantitative information flow. We show that the Bayes' capacity, related to the Sibson mutual information of order infinity, represents a tight upper bound on the leakage of the DP-SGD algorithm to an adversary interested in performing a reconstruction attack. We provide empirical results demonstrating the effectiveness of this measure for comparing mechanisms against reconstruction threats.
Trapezoidal Gradient Descent for Effective Reinforcement Learning in Spiking Networks
Authors: Yuhao Pan, Xiucheng Wang, Nan Cheng, Qi Qiu
Date: 2024-06-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Sensitivity

With the rapid development of artificial intelligence technology, the field of reinforcement learning has continuously achieved breakthroughs in both theory and practice. However, traditional reinforcement learning algorithms often entail high energy consumption during interactions with the environment. Spiking Neural Network (SNN), with their low energy consumption characteristics and performance comparable to deep neural networks, have garnered widespread attention. To reduce the energy consumption of practical applications of reinforcement learning, researchers have successively proposed the Pop-SAN and MDC-SAN algorithms. Nonetheless, these algorithms use rectangular functions to approximate the spike network during the training process, resulting in low sensitivity, thus indicating room for improvement in the training effectiveness of SNN. Based on this, we propose a trapezoidal approximation gradient method to replace the spike network, which not only preserves the original stable learning state but also enhances the model's adaptability and response sensitivity under various signal dynamics. Simulation results show that the improved algorithm, using the trapezoidal approximation gradient to replace the spike network, achieves better convergence speed and performance compared to the original algorithm and demonstrates good training stability.
Is AI fun? HumorDB: a curated dataset and benchmark to investigate graphical humor
Authors: Veedant Jain, Felipe dos Santos Alves Feitosa, Gabriel Kreiman
Date: 2024-06-19 00:00:00
Tasks: Binary Classification

Despite significant advancements in computer vision, understanding complex scenes, particularly those involving humor, remains a substantial challenge. This paper introduces HumorDB, a novel image-only dataset specifically designed to advance visual humor understanding. HumorDB consists of meticulously curated image pairs with contrasting humor ratings, emphasizing subtle visual cues that trigger humor and mitigating potential biases. The dataset enables evaluation through binary classification(Funny or Not Funny), range regression(funniness on a scale from 1 to 10), and pairwise comparison tasks(Which Image is Funnier?), effectively capturing the subjective nature of humor perception. Initial experiments reveal that while vision-only models struggle, vision-language models, particularly those leveraging large language models, show promising results. HumorDB also shows potential as a valuable zero-shot benchmark for powerful large multimodal models. We open-source both the dataset and code under the CC BY 4.0 license.
Enhancing Travel Choice Modeling with Large Language Models: A Prompt-Learning Approach
Authors: Xuehao Zhai, Hanlin Tian, Lintong Li, Tianyu Zhao
Date: 2024-06-19 00:00:00
Tasks: Discrete Choice Models, Language Modeling, Language Modelling, Large Language Model, Prompt Learning, Recommendation Systems

Travel choice analysis is crucial for understanding individual travel behavior to develop appropriate transport policies and recommendation systems in Intelligent Transportation Systems (ITS). Despite extensive research, this domain faces two critical challenges: a) modeling with limited survey data, and b) simultaneously achieving high model explainability and accuracy. In this paper, we introduce a novel prompt-learning-based Large Language Model(LLM) framework that significantly improves prediction accuracy and provides explicit explanations for individual predictions. This framework involves three main steps: transforming input variables into textual form; building of demonstrations similar to the object, and applying these to a well-trained LLM. We tested the framework's efficacy using two widely used choice datasets: London Passenger Mode Choice (LPMC) and Optima-Mode collected in Switzerland. The results indicate that the LLM significantly outperforms state-of-the-art deep learning methods and discrete choice models in predicting people's choices. Additionally, we present a case of explanation illustrating how the LLM framework generates understandable and explicit explanations at the individual level.
BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation
Authors: Minchong Li, Feng Zhou, Xiaohui Song
Date: 2024-06-19 00:00:00
Tasks: Knowledge Distillation, Language Modeling, Language Modelling, Large Language Model

In recent years, large language models (LLMs) have shown exceptional capabilities across various natural language processing (NLP) tasks. However, such impressive performance often comes with the trade-off of an increased parameter size, posing significant challenges for widespread deployment. Knowledge distillation (KD) provides a solution by transferring knowledge from a large teacher model to a smaller student model. In this paper, we explore the task-specific distillation of LLMs at the logit level. Our investigation reveals that the logits of fine-tuned LLMs exhibit a more extreme long-tail distribution than those from vision models, with hidden "noise" in the long tail affecting distillation performance. Furthermore, existing logits distillation methods often struggle to effectively utilize the internal ranking information from the logits. To address these, we propose the Bi-directional Logits Difference (BiLD) loss. The BiLD loss filters out the long-tail noise by utilizing only top-$k$ teacher and student logits, and leverages the internal logits ranking information by constructing logits differences. To evaluate BiLD loss, we conduct comprehensive experiments on 13 datasets using two types of LLMs. Our results show that the BiLD loss, with only the top-8 logits, outperforms supervised fine-tuning (SFT), vanilla KL loss, and five other distillation methods from both NLP and CV fields.
Mitigating Social Biases in Language Models through Unlearning
Authors: Omkar Dige, Diljot Singh, Tsz Fung Yau, Qixuan Zhang, Borna Bolandraftar, Xiaodan Zhu, Faiza Khan Khattak
Date: 2024-06-19 00:00:00
Tasks: Decoder, Machine Unlearning, Negation

Mitigating bias in language models (LMs) has become a critical problem due to the widespread deployment of LMs. Numerous approaches revolve around data pre-processing and fine-tuning of language models, tasks that can be both time-consuming and computationally demanding. Consequently, there is a growing interest in machine unlearning techniques given their capacity to induce the forgetting of undesired behaviors of the existing pre-trained or fine-tuned models with lower computational cost. In this work, we explore two unlearning methods, (1) Partitioned Contrastive Gradient Unlearning (PCGU) applied on decoder models and (2) Negation via Task Vector, to reduce social biases in state-of-the-art and open-source LMs such as LLaMA-2 and OPT. We also implement distributed PCGU for large models. It is empirically shown, through quantitative and qualitative analyses, that negation via Task Vector method outperforms PCGU in debiasing with minimum deterioration in performance and perplexity of the models. On LLaMA-27B, negation via Task Vector reduces the bias score by 11.8%
Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models
Authors: Guanting Dong, Keming Lu, Chengpeng Li, Tingyu Xia, Bowen Yu, Chang Zhou, Jingren Zhou
Date: 2024-06-19 00:00:00
Tasks: Instruction Following

One core capability of large language models (LLMs) is to follow natural language instructions. However, the issue of automatically constructing high-quality training data to enhance the complex instruction-following abilities of LLMs without manual annotation remains unresolved. In this paper, we introduce AutoIF, the first scalable and reliable method for automatically generating instruction-following training data. AutoIF transforms the validation of instruction-following data quality into code verification, requiring LLMs to generate instructions, the corresponding code to check the correctness of the instruction responses, and unit test samples to verify the code's correctness. Then, execution feedback-based rejection sampling can generate data for Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) training. AutoIF achieves significant improvements across three training algorithms, SFT, Offline DPO, and Online DPO, when applied to the top open-source LLMs, Qwen2 and LLaMA3, in self-alignment and strong-to-weak distillation settings. Our code is publicly available at https://github.com/QwenLM/AutoIF.