Searcharxiv⌕ Search

arXiv subjects

Yufei Li

Publications and source records attributed to Yufei Li.

54 records · Page 3Linked to original sources
arXiv2024

Safety Alignment in NLP Tasks: Weakly Aligned Summarization as an In-Context Attack

Recent developments in balancing the usefulness and safety of Large Language Models (LLMs) have raised a critical question: Are mainstream NLP tasks adequately aligned with safety consideration? Our study, focusing on safety-sensitive documents obtained through adversarial attacks, reveals significant disparities in the safety alignment of various NLP tasks. For instance, LLMs can effectively summarize malicious long documents but often refuse to translate them. This discrepancy highlights a previously unidentified vulnerability: attacks exploiting tasks with weaker safety alignment, like summarization, can potentially compromise the integrity of tasks traditionally deemed more robust, such as translation and question-answering (QA). Moreover, the concurrent use of multiple NLP tasks with lesser safety alignment increases the risk of LLMs inadvertently processing harmful content. We demonstrate these vulnerabilities in various safety-aligned LLMs, particularly Llama2 models, Gemini and GPT-4, indicating an urgent need for strengthening safety alignments across a broad spectrum of NLP tasks.

cs.CL↗
arXiv2024

Ring-Exchange Interaction Effects on Magnons in Dirac Magnet CoTiO$_3$

The magnetic interactions that determine magnetic order and magnon energies typically involve only two spins. While rare, multi-spin interactions can also appear in quantum magnets and be the driving force in the ground state selection and in the nature of its excitations. By performing time-domain terahertz and magneto-Raman spectroscopy measurements combined with theoretical modeling, we determine the origin of the magnon excitation gap in Dirac antiferromagnet CoTiO$_3$. By adding a ring-exchange interaction in a hexagonal plaquette of the honeycomb lattice to both an XXZ spin model and to a low energy spin-orbital flavor wave model, a gap is generated in the magnon spectrum at the Brillouin zone center. With this addition, the flavor wave model reproduces a large swath of experimental results including terahertz, Raman, inelastic neutron scattering, and magnetization experiments.

cond-mat.str-el↗
arXiv2024

Distantly-Supervised Joint Extraction with Noise-Robust Learning

Joint entity and relation extraction is a process that identifies entity pairs and their relations using a single model. We focus on the problem of joint extraction in distantly-labeled data, whose labels are generated by aligning entity mentions with the corresponding entity and relation tags using a knowledge base (KB). One key challenge is the presence of noisy labels arising from both incorrect entity and relation annotations, which significantly impairs the quality of supervised learning. Existing approaches, either considering only one source of noise or making decisions using external knowledge, cannot well-utilize significant information in the training data. We propose DENRL, a generalizable framework that 1) incorporates a lightweight transformer backbone into a sequence labeling scheme for joint tagging, and 2) employs a noise-robust framework that regularizes the tagging model with significant relation patterns and entity-relation dependencies, then iteratively self-adapts to instances with less noise from both sources. Surprisingly, experiments on two benchmark datasets show that DENRL, using merely its own parametric distribution and simple data-driven heuristics, outperforms large language model-based baselines by a large margin with better interpretability.

cs.CL↗
arXiv2024

Uncertainty Awareness of Large Language Models Under Code Distribution Shifts: A Benchmark Study

Large Language Models (LLMs) have been widely employed in programming language analysis to enhance human productivity. Yet, their reliability can be compromised by various code distribution shifts, leading to inconsistent outputs. While probabilistic methods are known to mitigate such impact through uncertainty calibration and estimation, their efficacy in the language domain remains underexplored compared to their application in image-based tasks. In this work, we first introduce a large-scale benchmark dataset, incorporating three realistic patterns of code distribution shifts at varying intensities. Then we thoroughly investigate state-of-the-art probabilistic methods applied to CodeLlama using these shifted code snippets. We observe that these methods generally improve the uncertainty awareness of CodeLlama, with increased calibration quality and higher uncertainty estimation~(UE) precision. However, our study further reveals varied performance dynamics across different criteria (e.g., calibration error vs misclassification detection) and trade-off between efficacy and efficiency, highlighting necessary methodological selection tailored to specific contexts.

cs.SE↗
arXiv2023

Strain-Tunable Magnetic Compensation Temperature of Epitaxial Tb$_3$Fe$_5$O$_{12}$ Thin Films

High-quality rare-earth iron garnet (ReIG) Tb$_3$Fe$_5$O$_{12}$ (TbIG) thin films are epitaxially grown on a series of (111)-oriented garnet substrates with various lattice constants. The coherent growth induces a substrate-dependent in-plane tensile or compressive strain in the TbIG film. Measurements of the anomalous Hall-like effect (AHLE) in TbIG/Pt heterostructures show that the compensation temperature of TbIG films monotonically changes with the film strain. The strain results in a variation of the distances between magnetic atoms in the TbIG crystal and therefore the corresponding exchange interactions. The latter is explicitly calculated as a function of the lattice strain based on density functional theory, reproducing the observed experimental results. This work provides a versatile way to optimize ReIG-based spin-orbit torque devices.

cond-mat.mtrl-sci↗
arXiv2023

R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics

Autonomous robotic systems, like autonomous vehicles and robotic search and rescue, require efficient on-device training for continuous adaptation of Deep Reinforcement Learning (DRL) models in dynamic environments. This research is fundamentally motivated by the need to understand and address the challenges of on-device real-time DRL, which involves balancing timing and algorithm performance under memory constraints, as exposed through our extensive empirical studies. This intricate balance requires co-optimizing two pivotal parameters of DRL training -- batch size and replay buffer size. Configuring these parameters significantly affects timing and algorithm performance, while both (unfortunately) require substantial memory allocation to achieve near-optimal performance. This paper presents R^3, a holistic solution for managing timing, memory, and algorithm performance in on-device real-time DRL training. R^3 employs (i) a deadline-driven feedback loop with dynamic batch sizing for optimizing timing, (ii) efficient memory management to reduce memory footprint and allow larger replay buffer sizes, and (iii) a runtime coordinator guided by heuristic analysis and a runtime profiler for dynamically adjusting memory resource reservations. These components collaboratively tackle the trade-offs in on-device DRL training, improving timing and algorithm performance while minimizing the risk of out-of-memory (OOM) errors. We implemented and evaluated R^3 extensively across various DRL frameworks and benchmarks on three hardware platforms commonly adopted by autonomous robotic systems. Additionally, we integrate R^3 with a popular realistic autonomous car simulator to demonstrate its real-world applicability. Evaluation results show that R^3 achieves efficacy across diverse platforms, ensuring consistent latency performance and timing predictability with minimal overhead.

cs.RO↗
arXiv2023

GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection

Logs play a crucial role in system monitoring and debugging by recording valuable system information, including events and states. Although various methods have been proposed to detect anomalies in log sequences, they often overlook the significance of considering relations among system components, such as services and users, which can be identified from log contents. Understanding these relations is vital for detecting anomalies and their underlying causes. To address this issue, we introduce GLAD, a Graph-based Log Anomaly Detection framework designed to detect relational anomalies in system logs. GLAD incorporates log semantics, relational patterns, and sequential patterns into a unified framework for anomaly detection. Specifically, GLAD first introduces a field extraction module that utilizes prompt-based few-shot learning to identify essential fields from log contents. Then GLAD constructs dynamic log graphs for sliding windows by interconnecting extracted fields and log events parsed from the log parser. These graphs represent events and fields as nodes and their relations as edges. Subsequently, GLAD utilizes a temporal-attentive graph edge anomaly detection model for identifying anomalous relations in these dynamic log graphs. This model employs a Graph Neural Network (GNN)-based encoder enhanced with transformers to capture content, structural and temporal features. We evaluate our proposed method on three datasets, and the results demonstrate the effectiveness of GLAD in detecting anomalies indicated by varying relational patterns.

cs.LG↗
arXiv2023

RT-LM: Uncertainty-Aware Resource Management for Real-Time Inference of Language Models

Recent advancements in language models (LMs) have gained substantial attentions on their capability to generate human-like responses. Though exhibiting a promising future for various applications such as conversation AI, these LMs face deployment challenges on various devices due to their extreme computational cost and unpredictable inference latency. Such varied inference latency, identified as a consequence of uncertainty intrinsic to the nature of language, can lead to computational inefficiency and degrade the overall performance of LMs, especially under high-traffic workloads. Unfortunately, the bandwidth of these uncertainty sources is extensive, complicating the prediction of latency and the effects emanating from such uncertainties. To understand and mitigate the impact of uncertainty on real-time response-demanding systems, we take the first step to comprehend, quantify and optimize these uncertainty-induced latency performance variations in LMs. Specifically, we present RT-LM, an uncertainty-aware resource management ecosystem for real-time inference of LMs. RT-LM innovatively quantifies how specific input uncertainties, adversely affect latency, often leading to an increased output length. Exploiting these insights, we devise a lightweight yet effective method to dynamically correlate input text uncertainties with output length at runtime. Utilizing this quantification as a latency heuristic, we integrate the uncertainty information into a system-level scheduler which explores several uncertainty-induced optimization opportunities, including uncertainty-aware prioritization, dynamic consolidation, and strategic CPU offloading. Quantitative experiments across five state-of-the-art LMs on two hardware platforms demonstrates that RT-LM can significantly reduce the average response time and improve throughput while incurring a rather small runtime overhead.

cs.LG↗
arXiv2023

The Impact of Downgrading Protected Areas (PAD) on Biodiversity

We quantitatively assess the impacts of Downgrading Protected Areas (PAD) on biodiversity in the U.S.. Results show that PAD events significantly reduce biodiversity. The proximity to PAD events decreases the biodiversity by 26.0% within 50 km compared with records of species further away from the PAD events. We observe an overall 32.3% decrease in abundance after those nearest PAD events are enacted. Abundance declines more in organisms living in contact with water and non-mammals. Species abundance is more sensitive to the negative impacts in areas where PAD events were later reversed, as well as in areas close to protected areas belonging to the International Union for Conservation of Nature (IUCN) category. The enacted PAD events between the period 1903 to 2018 in the U.S. lead to economic losses of approximately $689.95 million due to decrease in abundance. Our results contribute to the understanding on the impact of environmental interventions such as PAD events on biodiversity change and provide important implications on biodiversity conservation policies.

q-bio.PE↗
arXiv2023

PIMbot: Policy and Incentive Manipulation for Multi-Robot Reinforcement Learning in Social Dilemmas

Recent research has demonstrated the potential of reinforcement learning (RL) in enabling effective multi-robot collaboration, particularly in social dilemmas where robots face a trade-off between self-interests and collective benefits. However, environmental factors such as miscommunication and adversarial robots can impact cooperation, making it crucial to explore how multi-robot communication can be manipulated to achieve different outcomes. This paper presents a novel approach, namely PIMbot, to manipulating the reward function in multi-robot collaboration through two distinct forms of manipulation: policy and incentive manipulation. Our work introduces a new angle for manipulation in recent multi-agent RL social dilemmas that utilize a unique reward function for incentivization. By utilizing our proposed PIMbot mechanisms, a robot is able to manipulate the social dilemma environment effectively. PIMbot has the potential for both positive and negative impacts on the task outcome, where positive impacts lead to faster convergence to the global optimum and maximized rewards for any chosen robot. Conversely, negative impacts can have a detrimental effect on the overall task performance. We present comprehensive experimental results that demonstrate the effectiveness of our proposed methods in the Gazebo-simulated multi-robot environment. Our work provides insights into how inter-robot communication can be manipulated and has implications for various robotic applications. %, including robotics, transportation, and manufacturing.

cs.RO↗
arXiv2023

Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data

Jointly extracting entity pairs and their relations is challenging when working on distantly-supervised data with ambiguous or noisy labels. To mitigate such impact, we propose uncertainty-aware bootstrap learning, which is motivated by the intuition that the higher uncertainty of an instance, the more likely the model confidence is inconsistent with the ground truths. Specifically, we first explore instance-level data uncertainty to create an initial high-confident examples. Such subset serves as filtering noisy instances and facilitating the model to converge fast at the early stage. During bootstrap learning, we propose self-ensembling as a regularizer to alleviate inter-model uncertainty produced by noisy labels. We further define probability variance of joint tagging probabilities to estimate inner-model parametric uncertainty, which is used to select and build up new reliable training instances for the next iteration. Experimental results on two large datasets reveal that our approach outperforms existing strong baselines and related methods.

cs.CL↗
arXiv2023

White-Box Multi-Objective Adversarial Attack on Dialogue Generation

Pre-trained transformers are popular in state-of-the-art dialogue generation (DG) systems. Such language models are, however, vulnerable to various adversarial samples as studied in traditional tasks such as text classification, which inspires our curiosity about their robustness in DG systems. One main challenge of attacking DG models is that perturbations on the current sentence can hardly degrade the response accuracy because the unchanged chat histories are also considered for decision-making. Instead of merely pursuing pitfalls of performance metrics such as BLEU, ROUGE, we observe that crafting adversarial samples to force longer generation outputs benefits attack effectiveness -- the generated responses are typically irrelevant, lengthy, and repetitive. To this end, we propose a white-box multi-objective attack method called DGSlow. Specifically, DGSlow balances two objectives -- generation accuracy and length, via a gradient-based multi-objective optimizer and applies an adaptive searching mechanism to iteratively craft adversarial samples with only a few modifications. Comprehensive experiments on four benchmark datasets demonstrate that DGSlow could significantly degrade state-of-the-art DG models with a higher success rate than traditional accuracy-based methods. Besides, our crafted sentences also exhibit strong transferability in attacking other models.

cs.CL↗
arXiv2023

CM-CASL: Comparison-based Performance Modeling of Software Systems via Collaborative Active and Semisupervised Learning

Configuration tuning for large software systems is generally challenging due to the complex configuration space and expensive performance evaluation. Most existing approaches follow a two-phase process, first learning a regression-based performance prediction model on available samples and then searching for the configurations with satisfactory performance using the learned model. Such regression-based models often suffer from the scarcity of samples due to the enormous time and resources required to run a large software system with a specific configuration. Moreover, previous studies have shown that even a highly accurate regression-based model may fail to discern the relative merit between two configurations, whereas performance comparison is actually one fundamental strategy for configuration tuning. To address these issues, this paper proposes CM-CASL, a Comparison-based performance Modeling approach for software systems via Collaborative Active and Semisupervised Learning. CM-CASL learns a classification model that compares the performance of two given configurations, and enhances the samples through a collaborative labeling process by both human experts and classifiers using an integration of active and semisupervised learning. Experimental results demonstrate that CM-CASL outperforms two state-of-the-art performance modeling approaches in terms of both classification accuracy and rank accuracy, and thus provides a better performance model for the subsequent work of configuration tuning.

cs.SE↗
arXiv2022

SHARE: a System for Hierarchical Assistive Recipe Editing

The large population of home cooks with dietary restrictions is under-served by existing cooking resources and recipe generation models. To help them, we propose the task of controllable recipe editing: adapt a base recipe to satisfy a user-specified dietary constraint. This task is challenging, and cannot be adequately solved with human-written ingredient substitution rules or existing end-to-end recipe generation models. We tackle this problem with SHARE: a System for Hierarchical Assistive Recipe Editing, which performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients. By decoupling ingredient and step editing, our step generator can explicitly integrate the available ingredients. Experiments on the novel RecipePairs dataset -- 83K pairs of similar recipes where each recipe satisfies one of seven dietary constraints -- demonstrate that SHARE produces convincing, coherent recipes that are appropriate for a target dietary constraint. We further show through human evaluations and real-world cooking trials that recipes edited by SHARE can be easily followed by home cooks to create appealing dishes.

cs.CL↗
arXiv2021

Role of Magnon-Magnon Scattering in Magnon Polaron Spin Seebeck Effect

The spin Seebeck effect (SSE) signal of magnon polarons in bulk-Y3Fe5O12 (YIG)/Pt heterostructures is found to drastically change as a function of temperature. It appears as a dip in the total SSE signal at low temperatures, but as the temperature increases, the dip gradually decreases before turning to a peak. We attribute the observed dip-to-peak transition to the rapid rise of the four-magnon scattering rate. Our analysis provides important insights into the microscopic origin of the hybridized excitations and the overall temperature dependence of the SSE anomalies.

cond-mat.mtrl-sci↗
arXiv2021

Engineered Raman Lasing in Photonic Integrated Chalcogenide Microresonators

Chalcogenide glass (ChG) is an attractive material for integrated nonlinear photonics due to its wide transparency and high nonlinearity, and its capability of being directly deposited and patterned on Silicon wafer substrates. It has a singular Raman effect among amorphous materials. Yet, the Raman lasing performance in high quality and chip integrated ChG microresonators remains unexplored. Here, we demonstrate an engineered Raman lasing dynamic based on home developed photonic integrated high-Q ChG microresonators. With a quality factor above 10^6, we achieve the record-low lasing threshold 3.25 mW among integrated planar photonic platforms. Both the single-mode Raman lasers and a broadband Raman-Kerr comb are observed and characterized, which is dependent on the dispersion of our flexible photonic platform and engineered via tuning the waveguide geometric size. The tunability of such a chipscale Raman laser is also demonstrated through tuning the pump wavelength and tuning the operating temperature on the chip. This allows for the access of single-mode lasing at arbitrary wavelengths in the range 1615-1755 nm. Our results may contribute to the understanding of rich Raman and Kerr nonlinear interactions in dissipative and nonlinear microresonators, and on application aspect, may pave a way to chip-scale efficient Raman lasers that is highly desired in spectroscopic applications in the infrared.

physics.optics↗
arXiv2021

Estimating Predictive Uncertainty Under Program Data Distribution Shift

Deep learning (DL) techniques have achieved great success in predictive accuracy in a variety of tasks, but deep neural networks (DNNs) are shown to produce highly overconfident scores for even abnormal samples. Well-defined uncertainty indicates whether a model's output should (or should not) be trusted and thus becomes critical in real-world scenarios which typically involves shifted input distributions due to many factors. Existing uncertainty approaches assume that testing samples from a different data distribution would induce unreliable model predictions thus have higher uncertainty scores. They quantify model uncertainty by calibrating DL model's confidence of a given input and evaluate the effectiveness in computer vision (CV) and natural language processing (NLP)-related tasks. However, their methodologies' reliability may be compromised under programming tasks due to difference in data representations and shift patterns. In this paper, we first define three different types of distribution shift in program data and build a large-scale shifted Java dataset. We implement two common programming language tasks on our dataset to study the effect of each distribution shift on DL model performance. We also propose a large-scale benchmark of existing state-of-the-art predictive uncertainty on programming tasks and investigate their effectiveness under data distribution shift. Experiments show that program distribution shift does degrade the DL model performance to varying degrees and that existing uncertainty methods all present certain limitations in quantifying uncertainty on program dataset.

cs.LG↗
arXiv2021

GLIB: Towards Automated Test Oracle for Graphically-Rich Applications

Graphically-rich applications such as games are ubiquitous with attractive visual effects of Graphical User Interface (GUI) that offers a bridge between software applications and end-users. However, various types of graphical glitches may arise from such GUI complexity and have become one of the main component of software compatibility issues. Our study on bug reports from game development teams in NetEase Inc. indicates that graphical glitches frequently occur during the GUI rendering and severely degrade the quality of graphically-rich applications such as video games. Existing automated testing techniques for such applications focus mainly on generating various GUI test sequences and check whether the test sequences can cause crashes. These techniques require constant human attention to captures non-crashing bugs such as bugs causing graphical glitches. In this paper, we present the first step in automating the test oracle for detecting non-crashing bugs in graphically-rich applications. Specifically, we propose \texttt{GLIB} based on a code-based data augmentation technique to detect game GUI glitches. We perform an evaluation of \texttt{GLIB} on 20 real-world game apps (with bug reports available) and the result shows that \texttt{GLIB} can achieve 100\% precision and 99.5\% recall in detecting non-crashing bugs such as game GUI glitches. Practical application of \texttt{GLIB} on another 14 real-world games (without bug reports) further demonstrates that \texttt{GLIB} can effectively uncover GUI glitches, with 48 of 53 bugs reported by \texttt{GLIB} having been confirmed and fixed so far.

cs.SE↗