pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging small language models for Text2SPARQL tasks to improve the resilience of AI assistance
Authors: Felix Brei, Johannes Frey, Lars-Peter Meyer
Date: 2024-05-27 00:00:00
Tasks: Knowledge Graphs, Text2Sparql

In this work we will show that language models with less than one billion parameters can be used to translate natural language to SPARQL queries after fine-tuning. Using three different datasets ranging from academic to real world, we identify prerequisites that the training data must fulfill in order for the training to be successful. The goal is to empower users of semantic web technology to use AI assistance with affordable commodity hardware, making them more resilient against external factors.
A novel framework for systematic propositional formula simplification based on existential graphs
Authors: Jordina Francès de Mas, Juliana Bowles
Date: 2024-05-27 00:00:00

This paper presents a novel simplification calculus for propositional logic derived from Peirce's existential graphs' rules of inference and implication graphs. Our rules can be applied to propositional logic formulae in nested form, are equivalence-preserving, guarantee a monotonically decreasing number of variables, clauses and literals, and maximise the preservation of structural problem information. Our techniques can also be seen as higher-level SAT preprocessing, and we show how one of our rules (TWSR) generalises and streamlines most of the known equivalence-preserving SAT preprocessing methods. In addition, we propose a simplification procedure based on the systematic application of two of our rules (EPR and TWSR) which is solver-agnostic and can be used to simplify large Boolean satisfiability problems and propositional formulae in arbitrary form, and we provide a formal analysis of its algorithmic complexity in terms of space and time. Finally, we show how our rules can be further extended with a novel n-ary implication graph to capture all known equivalence-preserving preprocessing procedures.
Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization
Authors: Dixuan Wang, Yanda Li, Junyuan Jiang, Zepeng Ding, Ziqin Luo, Guochao Jiang, Jiaqing Liang, Deqing Yang
Date: 2024-05-27 00:00:00

Large Language Models (LLMs) have shown remarkable capabilities in language understanding and generation. Nonetheless, it was also witnessed that LLMs tend to produce inaccurate responses to specific queries. This deficiency can be traced to the tokenization step LLMs must undergo, which is an inevitable limitation inherent to all LLMs. In fact, incorrect tokenization is the critical point that hinders LLMs in understanding the input precisely, thus leading to unsatisfactory output. This defect is more obvious in Chinese scenarios. To demonstrate this flaw of LLMs, we construct an adversarial dataset, named as $\textbf{ADT (Adversarial Dataset for Tokenizer)}$, which draws upon the vocabularies of various open-source LLMs to challenge LLMs' tokenization. ADT consists of two subsets: the manually constructed ADT-Human and the automatically generated ADT-Auto. Our empirical results reveal that our ADT is highly effective on challenging the tokenization of leading LLMs, including GPT-4o, Llama-3, Deepseek-R1 and so on, thus degrading these LLMs' capabilities. Moreover, our method of automatic data generation has been proven efficient and robust, which can be applied to any open-source LLMs. In this paper, we substantially investigate LLMs' vulnerability in terms of challenging their token segmentation, which will shed light on the subsequent research of improving LLMs' capabilities through optimizing their tokenization process and algorithms.
Graph Neural Networks on Quantum Computers
Authors: Yidong Liao, Xiao-Ming Zhang, Chris Ferrie
Date: 2024-05-27 00:00:00
Tasks: Graph Attention, Graph Neural Network, Quantum Machine Learning, Recommendation Systems

Graph Neural Networks (GNNs) are powerful machine learning models that excel at analyzing structured data represented as graphs, demonstrating remarkable performance in applications like social network analysis and recommendation systems. However, classical GNNs face scalability challenges when dealing with large-scale graphs. This paper proposes frameworks for implementing GNNs on quantum computers to potentially address the challenges. We devise quantum algorithms corresponding to the three fundamental types of classical GNNs: Graph Convolutional Networks, Graph Attention Networks, and Message-Passing GNNs. A complexity analysis of our quantum implementation of the Simplified Graph Convolutional (SGC) Network shows potential quantum advantages over its classical counterpart, with significant improvements in time and space complexities. Our complexities can have trade-offs between the two: when optimizing for minimal circuit depth, our quantum SGC achieves logarithmic time complexity in the input sizes (albeit at the cost of linear space complexity). When optimizing for minimal qubit usage, the quantum SGC exhibits space complexity logarithmic in the input sizes, offering an exponential reduction compared to classical SGCs, while still maintaining better time complexity. These results suggest our Quantum GNN frameworks could efficiently process large-scale graphs. This work paves the way for implementing more advanced Graph Neural Network models on quantum computers, opening new possibilities in quantum machine learning for analyzing graph-structured data.
ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation
Authors: Houxing Ren, Mingjie Zhan, Zhongyuan Wu, Aojun Zhou, Junting Pan, Hongsheng Li
Date: 2024-05-27 00:00:00
Tasks: Code Generation, HumanEval, Mathematical Reasoning, mbpp

Code generation plays a crucial role in various tasks, such as code auto-completion and mathematical reasoning. Previous work has proposed numerous methods to enhance code generation performance, including integrating feedback from the compiler. Inspired by this, we present ReflectionCoder, a novel approach that effectively leverages reflection sequences constructed by integrating compiler feedback to improve one-off code generation performance. Furthermore, we propose reflection self-distillation and dynamically masked distillation to effectively utilize these reflection sequences. Extensive experiments on three benchmarks, i.e., HumanEval (+), MBPP (+), and MultiPl-E, demonstrate that models fine-tuned with our method achieve state-of-the-art performance. Notably, ReflectionCoder-DeepSeek-Coder-33B reaches pass@1 of 82.9 (76.8) on HumanEval (+) and 84.1 (72.0) on MBPP (+), on par with GPT-3.5-Turbo and Claude-3-opus, and surpasses early GPT-4. Beyond the code domain, we believe this approach can benefit other domains that focus on final results and require long reasoning paths. Code and data are available at https://github.com/SenseLLM/ReflectionCoder.
WirelessLLM: Empowering Large Language Models Towards Wireless Intelligence
Authors: Jiawei Shao, Jingwen Tong, Qiong Wu, Wei Guo, Zijian Li, Zehong Lin, Jun Zhang
Date: 2024-05-27 00:00:00
Tasks: Prompt Engineering, Retrieval-augmented Generation

The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed, configured, and managed. Recent advancements in Large Language Models (LLMs) have sparked interest in their potential to revolutionize wireless communication systems. However, existing studies on LLMs for wireless systems are limited to a direct application for telecom language understanding. To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks. We first identify three foundational principles that underpin WirelessLLM: knowledge alignment, knowledge fusion, and knowledge evolution. Then, we investigate the enabling technologies to build WirelessLLM, including prompt engineering, retrieval augmented generation, tool usage, multi-modal pre-training, and domain-specific fine-tuning. Moreover, we present three case studies to demonstrate the practical applicability and benefits of WirelessLLM for solving typical problems in wireless networks. Finally, we conclude this paper by highlighting key challenges and outlining potential avenues for future research.
BeamVQ: Aligning Space-Time Forecasting Model via Self-training on Physics-aware Metrics
Authors: Hao Wu, Xingjian Shi, Ziyue Huang, Penghao Zhao, Wei Xiong, Jinbao Xue, Yangyu Tao, Xiaomeng Huang, Weiyan Wang
Date: 2024-05-27 00:00:00
Tasks: Decoder, Quantization

Data-driven deep learning has emerged as the new paradigm to model complex physical space-time systems. These data-driven methods learn patterns by optimizing statistical metrics and tend to overlook the adherence to physical laws, unlike traditional model-driven numerical methods. Thus, they often generate predictions that are not physically realistic. On the other hand, by sampling a large amount of high quality predictions from a data-driven model, some predictions will be more physically plausible than the others and closer to what will happen in the future. Based on this observation, we propose \emph{Beam search by Vector Quantization} (BeamVQ) to enhance the physical alignment of data-driven space-time forecasting models. The key of BeamVQ is to train model on self-generated samples filtered with physics-aware metrics. To be flexibly support different backbone architectures, BeamVQ leverages a code bank to transform any encoder-decoder model to the continuous state space into discrete codes. Afterwards, it iteratively employs beam search to sample high-quality sequences, retains those with the highest physics-aware scores, and trains model on the new dataset. Comprehensive experiments show that BeamVQ not only gave an average statistical skill score boost for more than 32% for ten backbones on five datasets, but also significantly enhances physics-aware metrics.
Interesting Scientific Idea Generation using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders
Authors: Xuemei Gu, Mario Krenn
Date: 2024-05-27 00:00:00
Tasks: Knowledge Graphs, Language Modeling, Language Modelling, Large Language Model

The rapid growth of scientific literature makes it challenging for researchers to identify novel and impactful ideas, especially across disciplines. Modern artificial intelligence (AI) systems offer new approaches, potentially inspiring ideas not conceived by humans alone. But how compelling are these AI-generated ideas, and how can we improve their quality? Here, we introduce SciMuse, which uses 58 million research papers and a large-language model to generate research ideas. We conduct a large-scale evaluation in which over 100 research group leaders -- from natural sciences to humanities -- ranked more than 4,400 personalized ideas based on their interest. This data allows us to predict research interest using (1) supervised neural networks trained on human evaluations, and (2) unsupervised zero-shot ranking with large-language models. Our results demonstrate how future systems can help generating compelling research ideas and foster unforeseen interdisciplinary collaborations.
Advancements in Tactile Hand Gesture Recognition for Enhanced Human-Machine Interaction
Authors: Chiara Fumelli, Anirvan Dutta, Mohsen Kaboli
Date: 2024-05-27 00:00:00
Tasks: Feature Engineering, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Motivated by the growing interest in enhancing intuitive physical Human-Machine Interaction (HRI/HVI), this study aims to propose a robust tactile hand gesture recognition system. We performed a comprehensive evaluation of different hand gesture recognition approaches for a large area tactile sensing interface (touch interface) constructed from conductive textiles. Our evaluation encompassed traditional feature engineering methods, as well as contemporary deep learning techniques capable of real-time interpretation of a range of hand gestures, accommodating variations in hand sizes, movement velocities, applied pressure levels, and interaction points. Our extensive analysis of the various methods makes a significant contribution to tactile-based gesture recognition in the field of human-machine interaction.
FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks
Authors: Renqiang Luo, Huafei Huang, Shuo Yu, Zhuoyang Han, Estrid He, Xiuzhen Zhang, Feng Xia
Date: 2024-05-27 00:00:00
Tasks: Fairness, Graph Learning

Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory, aiming to reconcile fairness and utility within the framework of spectral graph learning. We explore the correlation between sensitive features and spectrum in GNNs, using theoretical analysis to delineate the similarity between original sensitive features and those after convolution under different spectra. Our analysis reveals a reduction in the impact of similarity when the eigenvectors associated with the largest magnitude eigenvalue exhibit directional similarity. Based on these theoretical insights, we propose FUGNN, a novel spectral graph learning approach that harmonizes the conflict between fairness and utility. FUGNN ensures algorithmic fairness and utility by truncating the spectrum and optimizing eigenvector distribution during the encoding process. The fairness-aware eigenvector selection reduces the impact of convolution on sensitive features while concurrently minimizing the sacrifice of utility. FUGNN further optimizes the distribution of eigenvectors through a transformer architecture. By incorporating the optimized spectrum into the graph convolution network, FUGNN effectively learns node representations. Experiments on six real-world datasets demonstrate the superiority of FUGNN over baseline methods. The codes are available at https://github.com/yushuowiki/FUGNN.
SWAT: Scalable and Efficient Window Attention-based Transformers Acceleration on FPGAs
Authors: Zhenyu Bai, Pranav Dangi, Huize Li, Tulika Mitra
Date: 2024-05-27 00:00:00
Tasks: GPU

Efficiently supporting long context length is crucial for Transformer models. The quadratic complexity of the self-attention computation plagues traditional Transformers. Sliding window-based static sparse attention mitigates the problem by limiting the attention scope of the input tokens, reducing the theoretical complexity from quadratic to linear. Although the sparsity induced by window attention is highly structured, it does not align perfectly with the microarchitecture of the conventional accelerators, leading to suboptimal implementation. In response, we propose a dataflow-aware FPGA-based accelerator design, SWAT, that efficiently leverages the sparsity to achieve scalable performance for long input. The proposed microarchitecture is based on a design that maximizes data reuse by using a combination of row-wise dataflow, kernel fusion optimization, and an input-stationary design considering the distributed memory and computation resources of FPGA. Consequently, it achieves up to 22$\times$ and 5.7$\times$ improvement in latency and energy efficiency compared to the baseline FPGA-based accelerator and 15$\times$ energy efficiency compared to GPU-based solution.
Compositional Few-Shot Class-Incremental Learning
Authors: Yixiong Zou, Shanghang Zhang, Haichen Zhou, Yuhua Li, Ruixuan Li
Date: 2024-05-27 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Few-Shot Class-Incremental Learning, Incremental Learning

Few-shot class-incremental learning (FSCIL) is proposed to continually learn from novel classes with only a few samples after the (pre-)training on base classes with sufficient data. However, this remains a challenge. In contrast, humans can easily recognize novel classes with a few samples. Cognitive science demonstrates that an important component of such human capability is compositional learning. This involves identifying visual primitives from learned knowledge and then composing new concepts using these transferred primitives, making incremental learning both effective and interpretable. To imitate human compositional learning, we propose a cognitive-inspired method for the FSCIL task. We define and build a compositional model based on set similarities, and then equip it with a primitive composition module and a primitive reuse module. In the primitive composition module, we propose to utilize the Centered Kernel Alignment (CKA) similarity to approximate the similarity between primitive sets, allowing the training and evaluation based on primitive compositions. In the primitive reuse module, we enhance primitive reusability by classifying inputs based on primitives replaced with the closest primitives from other classes. Experiments on three datasets validate our method, showing it outperforms current state-of-the-art methods with improved interpretability. Our code is available at https://github.com/Zoilsen/Comp-FSCIL.
Position: Foundation Agents as the Paradigm Shift for Decision Making
Authors: Xiaoqian Liu, Xingzhou Lou, Jianbin Jiao, Junge Zhang
Date: 2024-05-27 00:00:00
Tasks: Decision Making, Position

Decision making demands intricate interplay between perception, memory, and reasoning to discern optimal policies. Conventional approaches to decision making face challenges related to low sample efficiency and poor generalization. In contrast, foundation models in language and vision have showcased rapid adaptation to diverse new tasks. Therefore, we advocate for the construction of foundation agents as a transformative shift in the learning paradigm of agents. This proposal is underpinned by the formulation of foundation agents with their fundamental characteristics and challenges motivated by the success of large language models (LLMs). Moreover, we specify the roadmap of foundation agents from large interactive data collection or generation, to self-supervised pretraining and adaptation, and knowledge and value alignment with LLMs. Lastly, we pinpoint critical research questions derived from the formulation and delineate trends for foundation agents supported by real-world use cases, addressing both technical and theoretical aspects to propel the field towards a more comprehensive and impactful future.
Vision-and-Language Navigation Generative Pretrained Transformer
Authors: Wen Hanlin
Date: 2024-05-27 00:00:00
Tasks: Decoder, Imitation Learning, Vision and Language Navigation

In the Vision-and-Language Navigation (VLN) field, agents are tasked with navigating real-world scenes guided by linguistic instructions. Enabling the agent to adhere to instructions throughout the process of navigation represents a significant challenge within the domain of VLN. To address this challenge, common approaches often rely on encoders to explicitly record past locations and actions, increasing model complexity and resource consumption. Our proposal, the Vision-and-Language Navigation Generative Pretrained Transformer (VLN-GPT), adopts a transformer decoder model (GPT2) to model trajectory sequence dependencies, bypassing the need for historical encoding modules. This method allows for direct historical information access through trajectory sequence, enhancing efficiency. Furthermore, our model separates the training process into offline pre-training with imitation learning and online fine-tuning with reinforcement learning. This distinction allows for more focused training objectives and improved performance. Performance assessments on the VLN dataset reveal that VLN-GPT surpasses complex state-of-the-art encoder-based models.
Exploring the LLM Journey from Cognition to Expression with Linear Representations
Authors: Yuzi Yan, Jialian Li, Yipin Zhang, Dong Yan
Date: 2024-05-27 00:00:00
Tasks: Few-Shot Learning

This paper presents an in-depth examination of the evolution and interplay of cognitive and expressive capabilities in large language models (LLMs), with a specific focus on Baichuan-7B and Baichuan-33B, an advanced bilingual (Chinese and English) LLM series. We define and explore the model's cognitive and expressive capabilities through linear representations across three critical phases: Pretraining, Supervised Fine-Tuning (SFT), and Reinforcement Learning from Human Feedback (RLHF). Cognitive capability is defined as the quantity and quality of information conveyed by the neuron output vectors within the network, similar to the neural signal processing in human cognition. Expressive capability is defined as the model's capability to produce word-level output. Our findings unveil a sequential development pattern, where cognitive abilities are largely established during Pretraining, whereas expressive abilities predominantly advance during SFT and RLHF. Statistical analyses confirm a significant correlation between the two capabilities, suggesting that cognitive capacity may limit expressive potential. The paper also explores the theoretical underpinnings of these divergent developmental trajectories and their connection to the LLMs' architectural design. Moreover, we evaluate various optimization-independent strategies, such as few-shot learning and repeated sampling, which bridge the gap between cognitive and expressive capabilities. This research reveals the potential connection between the hidden space and the output space, contributing valuable insights into the interpretability and controllability of their training processes.
Blind Data Adaptation to tackle Covariate Shift in Operational Steganalysis
Authors: Rony Abecidan, Vincent Itier, Jérémie Boulanger, Patrick Bas, Tomáš Pevný
Date: 2024-05-27 00:00:00
Tasks: Image Manipulation, Image Steganography, Steganalysis

The proliferation of image manipulation for unethical purposes poses significant challenges in social networks. One particularly concerning method is Image Steganography, allowing individuals to hide illegal information in digital images without arousing suspicions. Such a technique pose severe security risks, making it crucial to develop effective steganalysis methods enabling to detect manipulated images for clandestine communications. Although significant advancements have been achieved with machine learning models, a critical issue remains: the disparity between the controlled datasets used to train steganalysis models against real-world datasets of forensic practitioners, undermining severely the practical effectiveness of standardized steganalysis models. In this paper, we address this issue focusing on a realistic scenario where practitioners lack crucial information about the limited target set of images under analysis, including details about their development process and even whereas it contains manipulated images or not. By leveraging geometric alignment and distribution matching of source and target residuals, we develop TADA (Target Alignment through Data Adaptation), a novel methodology enabling to emulate sources aligned with specific targets in steganalysis, which is also relevant for highly unbalanced targets. The emulator is represented by a light convolutional network trained to align distributions of image residuals. Experimental validation demonstrates the potential of our strategy over traditional methods fighting covariate shift in steganalysis.