pwc-archive/papers-with-abstracts huggingface dataset:
Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation
Authors: Junhong Wu, Yang Zhao, Yangyifan Xu, Bing Liu, Chengqing Zong
Date: 2024-10-17 00:00:00
Tasks: General Knowledge, Instruction Following, Machine Translation, Translation

Large Language Models (LLMs) have achieved impressive results across numerous NLP tasks but still encounter difficulties in machine translation. Traditional methods to improve translation have typically involved fine-tuning LLMs using parallel corpora. However, vanilla fine-tuning often leads to catastrophic forgetting of the instruction-following capabilities and alignment with human preferences, compromising their broad general abilities and introducing potential security risks. These abilities, which are developed using proprietary and unavailable training data, make existing continual instruction tuning methods ineffective. To overcome this issue, we propose a novel approach called RaDis (Rationale Distillation). RaDis harnesses the strong generative capabilities of LLMs to create rationales for training data, which are then "replayed" to prevent forgetting. These rationales encapsulate general knowledge and safety principles, acting as self-distillation targets to regulate the training process. By jointly training on both reference translations and self-generated rationales, the model can learn new translation skills while preserving its overall general abilities. Extensive experiments demonstrate that our method enhances machine translation performance while maintaining the broader capabilities of LLMs across other tasks. This work presents a pathway for creating more versatile LLMs that excel in specialized tasks without compromising generality and safety.
Non-Invasive Qualitative Vibration Analysis using Event Camera
Authors: Dwijay Bane, Anurag Gupta, Manan Suri
Date: 2024-10-18 00:00:00
Tasks: Event-based vision, Motion Magnification

This technical report investigates the application of event-based vision sensors in non-invasive qualitative vibration analysis, with a particular focus on frequency measurement and motion magnification. Event cameras, with their high temporal resolution and dynamic range, offer promising capabilities for real-time structural assessment and subtle motion analysis. Our study employs cutting-edge event-based vision techniques to explore real-world scenarios in frequency measurement in vibrational analysis and intensity reconstruction for motion magnification. In the former, event-based sensors demonstrated significant potential for real-time structural assessment. However, our work in motion magnification revealed considerable challenges, particularly in scenarios involving stationary cameras and isolated motion.
"Efficient Complexity": a Constrained Optimization Approach to the Evolution of Natural Intelligence
Authors: Serge Dolgikh
Date: 2024-10-03 00:00:00

A fundamental question in the conjunction of information theory, biophysics, bioinformatics and thermodynamics relates to the principles and processes that guide the development of natural intelligence in natural environments where information about external stimuli may not be available at prior. A novel approach in the description of the information processes of natural learning is proposed in the framework of constrained optimization, where the objective function represented by the information entropy of the internal states of the system with the states of the external environment is maximized under the natural constraints of memory, computing power, energy and other essential resources. The progress of natural intelligence can be interpreted in this framework as a strategy of approximation of the solutions of the optimization problem via a traversal over the extrema network of the objective function under the natural constraints that were examined and described. Non-trivial conclusions on the relationships between the complexity, variability and efficiency of the structure, or architecture of learning models made on the basis of the proposed formalism can explain the effectiveness of neural networks as collaborative groups of small intelligent units in biological and artificial intelligence.
BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge Distillation
Authors: Zhengrui Guo, Fangxu Zhou, Wei Wu, Qichen Sun, Lishuang Feng, Jinzhuo Wang, Hao Chen
Date: 2024-10-02 00:00:00
Tasks: Knowledge Distillation, Time Series Analysis

Modeling the nonlinear dynamics of neuronal populations represents a key pursuit in computational neuroscience. Recent research has increasingly focused on jointly modeling neural activity and behavior to unravel their interconnections. Despite significant efforts, these approaches often necessitate either intricate model designs or oversimplified assumptions. Given the frequent absence of perfectly paired neural-behavioral datasets in real-world scenarios when deploying these models, a critical yet understudied research question emerges: how to develop a model that performs well using only neural activity as input at inference, while benefiting from the insights gained from behavioral signals during training? To this end, we propose BLEND, the behavior-guided neural population dynamics modeling framework via privileged knowledge distillation. By considering behavior as privileged information, we train a teacher model that takes both behavior observations (privileged features) and neural activities (regular features) as inputs. A student model is then distilled using only neural activity. Unlike existing methods, our framework is model-agnostic and avoids making strong assumptions about the relationship between behavior and neural activity. This allows BLEND to enhance existing neural dynamics modeling architectures without developing specialized models from scratch. Extensive experiments across neural population activity modeling and transcriptomic neuron identity prediction tasks demonstrate strong capabilities of BLEND, reporting over 50% improvement in behavioral decoding and over 15% improvement in transcriptomic neuron identity prediction after behavior-guided distillation. Furthermore, we empirically explore various behavior-guided distillation strategies within the BLEND framework and present a comprehensive analysis of effectiveness and implications for model performance.
Associative memory and dead neurons
Authors: Vladimir Fanaskov, Ivan Oseledets
Date: 2024-10-02 00:00:00

In "Large Associative Memory Problem in Neurobiology and Machine Learning," Dmitry Krotov and John Hopfield introduced a general technique for the systematic construction of neural ordinary differential equations with non-increasing energy or Lyapunov function. We study this energy function and identify that it is vulnerable to the problem of dead neurons. Each point in the state space where the neuron dies is contained in a non-compact region with constant energy. In these flat regions, energy function alone does not completely determine all degrees of freedom and, as a consequence, can not be used to analyze stability or find steady states or basins of attraction. We perform a direct analysis of the dynamical system and show how to resolve problems caused by flat directions corresponding to dead neurons: (i) all information about the state vector at a fixed point can be extracted from the energy and Hessian matrix (of Lagrange function), (ii) it is enough to analyze stability in the range of Hessian matrix, (iii) if steady state touching flat region is stable the whole flat region is the basin of attraction. The analysis of the Hessian matrix can be complicated for realistic architectures, so we show that for a slightly altered dynamical system (with the same structure of steady states), one can derive a diverse family of Lyapunov functions that do not have flat regions corresponding to dead neurons. In addition, these energy functions allow one to use Lagrange functions with Hessian matrices that are not necessarily positive definite and even consider architectures with non-symmetric feedforward and feedback connections.
Bin-Conditional Conformal Prediction of Fatalities from Armed Conflict
Authors: David Randahl, Jonathan P. Williams, Håvard Hegre
Date: 2024-10-18 00:00:00
Tasks: Conformal Prediction, Prediction, Prediction Intervals, quantile regression

Forecasting armed conflicts is a critical area of research with the potential to save lives and mitigate suffering. While existing forecasting models offer valuable point predictions, they often lack individual-level uncertainty estimates, limiting their usefulness for decision-making. Several approaches exist to estimate uncertainty, such as parametric and Bayesian prediction intervals, bootstrapping, quantile regression, but these methods often rely on restrictive assumptions, struggle to provide well-calibrated intervals across the full range of outcomes, or are computationally intensive. Conformal prediction offers a model-agnostic alternative that guarantees a user-specified level of coverage but typically provides only marginal coverage, potentially resulting in non-uniform coverage across different regions of the outcome space. In this paper, we introduce a novel extension called bin-conditional conformal prediction (BCCP), which enhances standard conformal prediction by ensuring consistent coverage rates across user-defined subsets (bins) of the outcome variable. We apply BCCP to simulated data as well as the forecasting of fatalities from armed conflicts, and demonstrate that it provides well-calibrated uncertainty estimates across various ranges of the outcome. Compared to standard conformal prediction, BCCP offers improved local coverage, though this comes at the cost of slightly wider prediction intervals.
Asymptotic spectrum of weighted sample covariance: another proof of spectrum convergence
Authors: Benoit Oriol
Date: 2024-10-18 00:00:00

We propose another proof of the high dimensional spectrum convergence of the weighted sample covariance, more concise and self-sufficient but with stronger, but reasonable assumptions. We explain and illustrates this theorem for different weight distributions and show how the spectrum behaves in finite samples with heavy tails. The general purpose is to provide a detailed introduction to the high dimensional spectrum of weighted sample covariance.
Adaptive Gradient Normalization and Independent Sampling for (Stochastic) Generalized-Smooth Optimization
Authors: Yufeng Yang, Erin Tripp, Yifan Sun, Shaofeng Zou, Yi Zhou
Date: 2024-10-17 00:00:00

Recent studies have shown that many nonconvex machine learning problems satisfy a generalized-smooth condition that extends beyond traditional smooth nonconvex optimization. However, the existing algorithms are not fully adapted to such generalized-smooth nonconvex geometry and encounter significant technical limitations on their convergence analysis. In this work, we first analyze the convergence of adaptively normalized gradient descent under function geometries characterized by generalized-smoothness and generalized P{\L} condition, revealing the advantage of adaptive gradient normalization. Our results provide theoretical insights into adaptive normalization across various scenarios.For stochastic generalized-smooth nonconvex optimization, we propose \textbf{I}ndependent-\textbf{A}daptively \textbf{N}ormalized \textbf{S}tochastic \textbf{G}radient \textbf{D}escent, which leverages adaptive gradient normalization, independent sampling, and gradient clipping to achieve an $\mathcal{O}(\epsilon^{-4})$ sample complexity under relaxed noise assumptions. Experiments on large-scale nonconvex generalized-smooth problems demonstrate the fast convergence of our algorithm.
SPFresh: Incremental In-Place Update for Billion-Scale Vector Search
Authors: Yuming Xu, Hengyu Liang, Jin Li, Shuotao Xu, Qi Chen, Qianxi Zhang, Cheng Li, Ziyue Yang, Fan Yang, Yuqing Yang, Peng Cheng, Mao Yang
Date: 2024-10-18 00:00:00
Tasks: Information Retrieval, Question Answering

Approximate Nearest Neighbor Search (ANNS) is now widely used in various applications, ranging from information retrieval, question answering, and recommendation, to search for similar high-dimensional vectors. As the amount of vector data grows continuously, it becomes important to support updates to vector index, the enabling technique that allows for efficient and accurate ANNS on vectors. Because of the curse of high dimensionality, it is often costly to identify the right neighbors of a single new vector, a necessary process for index update. To amortize update costs, existing systems maintain a secondary index to accumulate updates, which are merged by the main index by global rebuilding the entire index periodically. However, this approach has high fluctuations of search latency and accuracy, not even to mention that it requires substantial resources and is extremely time-consuming for rebuilds. We introduce SPFresh, a system that supports in-place vector updates. At the heart of SPFresh is LIRE, a lightweight incremental rebalancing protocol to split vector partitions and reassign vectors in the nearby partitions to adapt to data distribution shift. LIRE achieves low-overhead vector updates by only reassigning vectors at the boundary between partitions, where in a high-quality vector index the amount of such vectors are deemed small. With LIRE, SPFresh provides superior query latency and accuracy to solutions based on global rebuild, with only 1% of DRAM and less than 10% cores needed at the peak compared to the state-of-the-art, in a billion scale vector index with 1% of daily vector update rate.
ChartifyText: Automated Chart Generation from Data-Involved Texts via LLM
Authors: Songheng Zhang, Lei Wang, Toby Jia-Jun Li, Qiaomu Shen, Yixin Cao, Yong Wang
Date: 2024-10-18 00:00:00
Tasks: Prompt Engineering

Text documents with numerical values involved are widely used in various applications such as scientific research, economy, public health and journalism. However, it is difficult for readers to quickly interpret such data-involved texts and gain deep insights. To fill this research gap, this work aims to automatically generate charts to accurately convey the underlying data and ideas to readers, which is essentially a challenging task. The challenges originate from text ambiguities, intrinsic sparsity and uncertainty of data in text documents, and subjective sentiment differences. Specifically, we propose ChartifyText, a novel fully-automated approach that leverages Large Language Models (LLMs) to convert complex data-involved texts to expressive charts. It consists of two major modules: tabular data inference and expressive chart generation. The tabular data inference module employs systematic prompt engineering to guide the LLM (e.g., GPT-4) to infer table data, where data ranges, uncertainties, missing data values and corresponding subjective sentiments are explicitly considered. The expressive chart generation module augments standard charts with intuitive visual encodings and concise texts to accurately convey the underlying data and insights. We extensively evaluate the effectiveness of ChartifyText on real-world data-involved text documents through case studies, in-depth interviews with three visualization experts, and a carefully-designed user study with 15 participants. The results demonstrate the usefulness and effectiveness of ChartifyText in helping readers efficiently and effectively make sense of data-involved texts.
Graph Neural Patching for Cold-Start Recommendations
Authors: Hao Chen, Yu Yang, Yuanchen Bei, Zefan Wang, Yue Xu, Feiran Huang
Date: 2024-10-18 00:00:00
Tasks: Recommendation Systems

The cold start problem in recommender systems remains a critical challenge. Current solutions often train hybrid models on auxiliary data for both cold and warm users/items, potentially degrading the experience for the latter. This drawback limits their viability in practical scenarios where the satisfaction of existing warm users/items is paramount. Although graph neural networks (GNNs) excel at warm recommendations by effective collaborative signal modeling, they haven't been effectively leveraged for the cold-start issue within a user-item graph, which is largely due to the lack of initial connections for cold user/item entities. Addressing this requires a GNN adept at cold-start recommendations without sacrificing performance for existing ones. To this end, we introduce Graph Neural Patching for Cold-Start Recommendations (GNP), a customized GNN framework with dual functionalities: GWarmer for modeling collaborative signal on existing warm users/items and Patching Networks for simulating and enhancing GWarmer's performance on cold-start recommendations. Extensive experiments on three benchmark datasets confirm GNP's superiority in recommending both warm and cold users/items.
Personalized Image Generation with Large Multimodal Models
Authors: Yiyan Xu, Wenjie Wang, Yang Zhang, Biao Tang, Peng Yan, Fuli Feng, Xiangnan He
Date: 2024-10-18 00:00:00
Tasks: Image Generation, Personalized Image Generation, Recommendation Systems, Text Generation

Personalized content filtering, such as recommender systems, has become a critical infrastructure to alleviate information overload. However, these systems merely filter existing content and are constrained by its limited diversity, making it difficult to meet users' varied content needs. To address this limitation, personalized content generation has emerged as a promising direction with broad applications. Nevertheless, most existing research focuses on personalized text generation, with relatively little attention given to personalized image generation. The limited work in personalized image generation faces challenges in accurately capturing users' visual preferences and needs from noisy user-interacted images and complex multimodal instructions. Worse still, there is a lack of supervised data for training personalized image generation models. To overcome the challenges, we propose a Personalized Image Generation Framework named Pigeon, which adopts exceptional large multimodal models with three dedicated modules to capture users' visual preferences and needs from noisy user history and multimodal instructions. To alleviate the data scarcity, we introduce a two-stage preference alignment scheme, comprising masked preference reconstruction and pairwise preference alignment, to align Pigeon with the personalized image generation task. We apply Pigeon to personalized sticker and movie poster generation, where extensive quantitative results and human evaluation highlight its superiority over various generative baselines.
Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems
Authors: Jiajing Chen, Runyuan Bao, Hongye Zheng, Zhen Qi, Jianjun Wei, Jiacheng Hu
Date: 2024-10-18 00:00:00
Tasks: Question Answering, RAG, Retrieval, Retrieval-augmented Generation, Semantic Similarity, Semantic Textual Similarity

This study aims to improve the accuracy and quality of large-scale language models (LLMs) in answering questions by integrating Elasticsearch into the Retrieval Augmented Generation (RAG) framework. The experiment uses the Stanford Question Answering Dataset (SQuAD) version 2.0 as the test dataset and compares the performance of different retrieval methods, including traditional methods based on keyword matching or semantic similarity calculation, BM25-RAG and TF-IDF- RAG, and the newly proposed ES-RAG scheme. The results show that ES-RAG not only has obvious advantages in retrieval efficiency but also performs well in key indicators such as accuracy, which is 0.51 percentage points higher than TF-IDF-RAG. In addition, Elasticsearch's powerful search capabilities and rich configuration options enable the entire question-answering system to better handle complex queries and provide more flexible and efficient responses based on the diverse needs of users. Future research directions can further explore how to optimize the interaction mechanism between Elasticsearch and LLM, such as introducing higher-level semantic understanding and context-awareness capabilities, to achieve a more intelligent and humanized question-answering experience.
P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network
Authors: Zheng Wang, Wanwan Wang, Yimin Huang, Zhaopeng Peng, Ziqi Yang, Ming Yao, Cheng Wang, Xiaoliang Fan
Date: 2024-10-16 00:00:00
Tasks: Privacy Preserving, Recommendation Systems

In recent years, graph neural networks (GNNs) have been commonly utilized for social recommendation systems. However, real-world scenarios often present challenges related to user privacy and business constraints, inhibiting direct access to valuable social information from other platforms. While many existing methods have tackled matrix factorization-based social recommendations without direct social data access, developing GNN-based federated social recommendation models under similar conditions remains largely unexplored. To address this issue, we propose a novel vertical federated social recommendation method leveraging privacy-preserving two-party graph convolution networks (P4GCN) to enhance recommendation accuracy without requiring direct access to sensitive social information. First, we introduce a Sandwich-Encryption module to ensure comprehensive data privacy during the collaborative computing process. Second, we provide a thorough theoretical analysis of the privacy guarantees, considering the participation of both curious and honest parties. Extensive experiments on four real-world datasets demonstrate that P4GCN outperforms state-of-the-art methods in terms of recommendation accuracy.
Security of and by Generative AI platforms
Authors: Hari Hayagreevan, Souvik Khamaru
Date: 2024-10-15 00:00:00

This whitepaper highlights the dual importance of securing generative AI (genAI) platforms and leveraging genAI for cybersecurity. As genAI technologies proliferate, their misuse poses significant risks, including data breaches, model tampering, and malicious content generation. Securing these platforms is critical to protect sensitive data, ensure model integrity, and prevent adversarial attacks. Simultaneously, genAI presents opportunities for enhancing security by automating threat detection, vulnerability analysis, and incident response. The whitepaper explores strategies for robust security frameworks around genAI systems, while also showcasing how genAI can empower organizations to anticipate, detect, and mitigate sophisticated cyber threats.
LogKG: Log Failure Diagnosis Through Knowledge Graph
Authors: Yicheng Sui, Yuzhe Zhang, Jianjun Sun, Ting Xu, Shenglin Zhang, Zhengdan Li, Yongqian Sun, Member, Fangrui Guo, Junyu Shen, Yuzhi Zhang, Dan Pei, Senior Member, IEEE, Xiao Yang, and Li Yu
Date: 2023-07-11 00:00:00
Proceeding: IEEE Transactions on Services Computing 2023 7
Tasks: Knowledge Graphs

Logs are one of the most valuable data to describe the running state of services. Failure diagnosis through logs is crucial for service reliability and security. The current automatic log failure diagnosis methods cannot fully use the multiple fields of logs, which fail to capture the relation between them. In this article, we propose LogKG, a new framework for diagnosing failures based on knowledge graphs (KG) of logs. LogKG fully extracts entities and relations from logs to mine multi-field information and their relations through the KG. To fully use the information represented by KG, we propose a failure-oriented log representation (FOLR) method to extract the failure-related patterns. Utilizing the OPTICS clustering method, LogKG aggregates historical failure cases, labels typical failure cases, and trains a failure diagnosis model to identify the root cause. We evaluate the effectiveness of LogKG on a real-world log dataset and a public log dataset, respectively, showing that it outperforms existing methods. With the deployment in a top-tier global Internet Service Provider (ISP), we demonstrate the performance and practicability of LogKG.