pwc-archive/papers-with-abstracts huggingface dataset:
ChatBug: A Common Vulnerability of Aligned LLMs Induced by Chat Templates
Authors: Fengqing Jiang, Zhangchen Xu, Luyao Niu, Bill Yuchen Lin, Radha Poovendran
Date: 2024-06-17 00:00:00
Tasks: Instruction Following, Safety Alignment

Large language models (LLMs) are expected to follow instructions from users and engage in conversations. Techniques to enhance LLMs' instruction-following capabilities typically fine-tune them using data structured according to a predefined chat template. Although chat templates are shown to be effective in optimizing LLM performance, their impact on safety alignment of LLMs has been less understood, which is crucial for deploying LLMs safely at scale. In this paper, we investigate how chat templates affect safety alignment of LLMs. We identify a common vulnerability, named ChatBug, that is introduced by chat templates. Our key insight to identify ChatBug is that the chat templates provide a rigid format that need to be followed by LLMs, but not by users. Hence, a malicious user may not necessarily follow the chat template when prompting LLMs. Instead, malicious users could leverage their knowledge of the chat template and accordingly craft their prompts to bypass safety alignments of LLMs. We develop two attacks to exploit the ChatBug vulnerability. We demonstrate that a malicious user can exploit the ChatBug vulnerability of eight state-of-the-art (SOTA) LLMs and effectively elicit unintended responses from these models. Moreover, we show that ChatBug can be exploited by existing jailbreak attacks to enhance their attack success rates. We investigate potential countermeasures to ChatBug. Our results show that while adversarial training effectively mitigates the ChatBug vulnerability, the victim model incurs significant performance degradation. These results highlight the trade-off between safety alignment and helpfulness. Developing new methods for instruction tuning to balance this trade-off is an open and critical direction for future research
Current state of LLM Risks and AI Guardrails
Authors: Suriya Ganesh Ayyamperumal, Limin Ge
Date: 2024-06-16 00:00:00
Tasks: Fairness, RAG, Retrieval-augmented Generation

Large language models (LLMs) have become increasingly sophisticated, leading to widespread deployment in sensitive applications where safety and reliability are paramount. However, LLMs have inherent risks accompanying them, including bias, potential for unsafe actions, dataset poisoning, lack of explainability, hallucinations, and non-reproducibility. These risks necessitate the development of "guardrails" to align LLMs with desired behaviors and mitigate potential harm. This work explores the risks associated with deploying LLMs and evaluates current approaches to implementing guardrails and model alignment techniques. We examine intrinsic and extrinsic bias evaluation methods and discuss the importance of fairness metrics for responsible AI development. The safety and reliability of agentic LLMs (those capable of real-world actions) are explored, emphasizing the need for testability, fail-safes, and situational awareness. Technical strategies for securing LLMs are presented, including a layered protection model operating at external, secondary, and internal levels. System prompts, Retrieval-Augmented Generation (RAG) architectures, and techniques to minimize bias and protect privacy are highlighted. Effective guardrail design requires a deep understanding of the LLM's intended use case, relevant regulations, and ethical considerations. Striking a balance between competing requirements, such as accuracy and privacy, remains an ongoing challenge. This work underscores the importance of continuous research and development to ensure the safe and responsible use of LLMs in real-world applications.
RMF: A Risk Measurement Framework for Machine Learning Models
Authors: Jan Schröder, Jakub Breier
Date: 2024-06-15 00:00:00
Tasks: Autonomous Vehicles

Machine learning (ML) models are used in many safety- and security-critical applications nowadays. It is therefore important to measure the security of a system that uses ML as a component. This paper focuses on the field of ML, particularly the security of autonomous vehicles. For this purpose, a technical framework will be described, implemented, and evaluated in a case study. Based on ISO/IEC 27004:2016, risk indicators are utilized to measure and evaluate the extent of damage and the effort required by an attacker. It is not possible, however, to determine a single risk value that represents the attacker's effort. Therefore, four different values must be interpreted individually.
Pattern or Artifact? Interactively Exploring Embedding Quality with TRACE
Authors: Edith Heiter, Liesbet Martens, Ruth Seurinck, Martin Guilliams, Tijl De Bie, Yvan Saeys, Jefrey Lijffijt
Date: 2024-06-18 00:00:00
Tasks: Dimensionality Reduction

This paper presents TRACE, a tool to analyze the quality of 2D embeddings generated through dimensionality reduction techniques. Dimensionality reduction methods often prioritize preserving either local neighborhoods or global distances, but insights from visual structures can be misleading if the objective has not been achieved uniformly. TRACE addresses this challenge by providing a scalable and extensible pipeline for computing both local and global quality measures. The interactive browser-based interface allows users to explore various embeddings while visually assessing the pointwise embedding quality. The interface also facilitates in-depth analysis by highlighting high-dimensional nearest neighbors for any group of points and displaying high-dimensional distances between points. TRACE enables analysts to make informed decisions regarding the most suitable dimensionality reduction method for their specific use case, by showing the degree and location where structure is preserved in the reduced space.
Guaranteed shares of benefits and costs
Authors: Anna Bogomolnaia, Hervé Moulin
Date: 2024-06-20 00:00:00

We revisit the self-ownership viewpoint to regulate the utilisation of common property resources. Allocating to each agent "the fruit of their own labor" is typically ill-depned, so we look for tight approximations of this decentralised ideal. For each agent i two guarantees limit, from above and below, the impact of other agents on i's allocation. They limit the range of unscripted negotiations, or the choice of a full sharing rule. Our context-free model of the commons is a mapping W from profiles of "types" to a freely transferable amount of benefit or cost. If W is super (resp. sub) modular there is a single tight upper (resp. lower) guarantee, and an infinite menu of tight lower (resp. upper) guarantees, each one conveying a precise normative viewpoint. We describe the menu for essentially all modular two person problems, and familiar examples like the allocation of an indivisible item, cooperative production, and facility location.
Nash equilibria of quasisupermodular games
Authors: Lu Yu
Date: 2024-06-19 00:00:00

We prove three results on the existence and structure of Nash equilibria for quasisupermodular games. A theorem is purely order-theoretic, and the other two involve topological hypotheses. Our topological results genralize Zhou's theorem (for supermodular games) and Calciano's theorem.
Joint Optimization of Switching Point and Power Control in Dynamic TDD Cell-Free Massive MIMO
Authors: Martin Andersson, Tung T. Vu, Pål Frenger, Erik G. Larsson
Date: 2024-06-20 00:00:00

We consider a cell-free massive multiple-input multiple-output (CFmMIMO) network operating in dynamic time division duplex (DTDD). The switching point between the uplink (UL) and downlink (DL) data transmission phases can be adapted dynamically to the instantaneous quality-of-service (QoS) requirements in order to improve energy efficiency (EE). To this end, we formulate a problem of optimizing the DTDD switching point jointly with the UL and DL power control coefficients, and the large-scale fading decoding (LSFD) weights for EE maximization. Then, we propose an iterative algorithm to solve the formulated challenging problem using successive convex approximation with an approximate stationary solution. Simulation results show that optimizing switching points remarkably improves EE compared with baseline schemes that adjust switching points heuristically.
Factor Graph Optimization of Error-Correcting Codes for Belief Propagation Decoding
Authors: Yoni Choukroun, Lior Wolf
Date: 2024-06-09 00:00:00

The design of optimal linear block codes capable of being efficiently decoded is of major concern, especially for short block lengths. As near capacity-approaching codes, Low-Density Parity-Check (LDPC) codes possess several advantages over other families of codes, the most notable being its efficient decoding via Belief Propagation. While many LDPC code design methods exist, the development of efficient sparse codes that meet the constraints of modern short code lengths and accommodate new channel models remains a challenge. In this work, we propose for the first time a gradient-based data-driven approach for the design of sparse codes. We develop locally optimal codes with respect to Belief Propagation decoding via the learning of the Factor graph under channel noise simulations. This is performed via a novel complete graph tensor representation of the Belief Propagation algorithm, optimized over finite fields via backpropagation and coupled with an efficient line-search method. The proposed approach is shown to outperform the decoding performance of existing popular codes by orders of magnitude and demonstrates the power of data-driven approaches for code design.
Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction
Authors: Wenzhao Jiang, Jindong Han, Hao liu, Tao Tao, Naiqiang Tan, Hui Xiong
Date: 2024-06-14 00:00:00
Tasks: Mixture-of-Experts, Prediction, Travel Time Estimation

Rapid urbanization has significantly escalated traffic congestion, underscoring the need for advanced congestion prediction services to bolster intelligent transportation systems. As one of the world's largest ride-hailing platforms, DiDi places great emphasis on the accuracy of congestion prediction to enhance the effectiveness and reliability of their real-time services, such as travel time estimation and route planning. Despite numerous efforts have been made on congestion prediction, most of them fall short in handling heterogeneous and dynamic spatio-temporal dependencies (e.g., periodic and non-periodic congestions), particularly in the presence of noisy and incomplete traffic data. In this paper, we introduce a Congestion Prediction Mixture-of-Experts, CP-MoE, to address the above challenges. We first propose a sparsely-gated Mixture of Adaptive Graph Learners (MAGLs) with congestion-aware inductive biases to improve the model capacity for efficiently capturing complex spatio-temporal dependencies in varying traffic scenarios. Then, we devise two specialized experts to help identify stable trends and periodic patterns within the traffic data, respectively. By cascading these experts with MAGLs, CP-MoE delivers congestion predictions in a more robust and interpretable manner. Furthermore, an ordinal regression strategy is adopted to facilitate effective collaboration among diverse experts. Extensive experiments on real-world datasets demonstrate the superiority of our proposed method compared with state-of-the-art spatio-temporal prediction models. More importantly, CP-MoE has been deployed in DiDi to improve the accuracy and reliability of the travel time estimation system.
PufferLib: Making Reinforcement Learning Libraries and Environments Play Nice
Authors: Joseph Suarez
Date: 2024-06-11 00:00:00
Tasks: NetHack, reinforcement-learning, Reinforcement Learning

You have an environment, a model, and a reinforcement learning library that are designed to work together but don't. PufferLib makes them play nice. The library provides one-line environment wrappers that eliminate common compatibility problems and fast vectorization to accelerate training. With PufferLib, you can use familiar libraries like CleanRL and SB3 to scale from classic benchmarks like Atari and Procgen to complex simulators like NetHack and Neural MMO. We release pip packages and prebuilt images with dependencies for dozens of environments. All of our code is free and open-source software under the MIT license, complete with baselines, documentation, and support at pufferai.github.io.
JavaBench: A Benchmark of Object-Oriented Code Generation for Evaluating Large Language Models
Authors: Jialun Cao, Zhiyong Chen, Jiarong Wu, Shing-Chi Cheung, Chang Xu
Date: 2024-06-10 00:00:00
Tasks: Benchmarking, Code Generation, HumanEval

Code generation benchmarks such as HumanEval are widely adopted to evaluate LLMs' capabilities. However, after consolidating the latest 24 benchmarks, we noticed three significant imbalances. First, imbalanced programming language. 95.8% of benchmarks involve Python, while only 5 benchmarks involve Java. Second, imbalanced code granularity. Function-/statement-level benchmarks account for over 83.3% of benchmarks. Only a mere handful extends to class-/project-levels, and all are limited to Python. Third, lacking advanced features. Existing benchmarks primarily assess basic coding skills, while overlooking advanced Object-Oriented Programming (OOP) features (i.e., encapsulation, inheritance, and polymorphism). To fill these gaps, we propose JavaBench, a project-level Java benchmark that exercises OOP features. It comprises four Java projects with 389 methods in 106 Java classes. The test coverage is up to 92%, and JavaBench is attested by 282 undergraduate students, reaching a 90.93/100 average score (i.e., pass rate against the test suite), ensuring the quality of documentation, code skeleton, and tests. To better evaluate LLM's capability against JavaBench, we introduce a systematic evaluation design covering three context settings and five synthesis strategies at two granularities using three hierarchical metrics. Our extensive experiment yields several interesting findings. First, we noticed that regarding project-level Java programming, LLMs are far behind undergraduate students (no project can be correctly completed by any studied LLMs, and at most 41.17% Pass@5 in a more relaxed evaluation). Second, using method signature as prompt context may strike an ideal balance for project-level code generation. JavaBench is publicly available at https://github.com/java-bench/JavaBench.
Safety-Critical Formation Control of Non-Holonomic Multi-Robot Systems in Communication-Limited Environments
Authors: Vishrut Bohara, Siavash Farzan
Date: 2024-06-19 00:00:00
Tasks: Collision Avoidance

This paper introduces a decentralized estimator-based safety-critical controller designed for formation control of non-holonomic mobile robots operating in communication-constrained environments. The proposed framework integrates a robust state estimator capable of accurately reconstructing neighboring agents' velocity vectors and orientations under varying dynamic conditions, with a decentralized formation tracking controller that leverages Control Barrier Functions (CBFs) to guarantee collision avoidance and inter-agent safety. We present a closed-form control law that ensures both stability and string stability, effectively attenuating disturbances propagating from leader to followers. The theoretical foundations of the estimator and controller are established using Lyapunov stability analysis, which confirms global asymptotic stability under constant velocities and global uniformly ultimate boundedness under time-varying conditions. Extensive numerical simulations and realistic Gazebo-based experiments validate the effectiveness, robustness, and practical applicability of the proposed method, demonstrating precise formation tracking, stringent safety maintenance, and disturbance resilience without relying on inter-robot communication.
Diffusion-based Generative Modeling with Discriminative Guidance for Streamable Speech Enhancement
Authors: Chenda Li, Samuele Cornell, Shinji Watanabe, Yanmin Qian
Date: 2024-06-19 00:00:00
Tasks: Speech Enhancement

Diffusion-based generative models (DGMs) have recently attracted attention in speech enhancement research (SE) as previous works showed a remarkable generalization capability. However, DGMs are also computationally intensive, as they usually require many iterations in the reverse diffusion process (RDP), making them impractical for streaming SE systems. In this paper, we propose to use discriminative scores from discriminative models in the first steps of the RDP. These discriminative scores require only one forward pass with the discriminative model for multiple RDP steps, thus greatly reducing computations. This approach also allows for performance improvements. We show that we can trade off between generative and discriminative capabilities as the number of steps with the discriminative score increases. Furthermore, we propose a novel streamable time-domain generative model with an algorithmic latency of 50 ms, which has no significant performance degradation compared to offline models.
Automatic Voice Classification Of Autistic Subjects
Authors: Jessica Vacca, Natascia Brondino, Fabio Dell'Acqua, Anna Vizziello, Pietro Savazzi
Date: 2024-06-19 00:00:00
Tasks: Classification

Autism Spectrum Disorders (ASD) describe a heterogeneous set of conditions classified as neurodevelopmental disorders. Although the mechanisms underlying ASD are not yet fully understood, more recent literature focused on multiple genetics and/or environmental risk factors. Heterogeneity of symptoms, especially in milder forms of this condition, could be a challenge for the clinician. In this work, an automatic speech classification algorithm is proposed to characterize the prosodic elements that best distinguish autism, to support the traditional diagnosis. The performance of the proposed algorithm is evaluted by testing the classification algorithms on a dataset composed of recorded speeches, collected among both autustic and non autistic subjects.
Online Domain-Incremental Learning Approach to Classify Acoustic Scenes in All Locations
Authors: Manjunath Mulimani, Annamaria Mesaros
Date: 2024-06-19 00:00:00
Tasks: Acoustic Scene Classification, All, Incremental Learning, Scene Classification

In this paper, we propose a method for online domain-incremental learning of acoustic scene classification from a sequence of different locations. Simply training a deep learning model on a sequence of different locations leads to forgetting of previously learned knowledge. In this work, we only correct the statistics of the Batch Normalization layers of a model using a few samples to learn the acoustic scenes from a new location without any excessive training. Experiments are performed on acoustic scenes from 11 different locations, with an initial task containing acoustic scenes from 6 locations and the remaining 5 incremental tasks each representing the acoustic scenes from a different location. The proposed approach outperforms fine-tuning based methods and achieves an average accuracy of 48.8% after learning the last task in sequence without forgetting acoustic scenes from the previously learned locations.
CEC: A Noisy Label Detection Method for Speaker Recognition
Authors: Yao Shen, Yingying Gao, Yaqian Hao, Chenguang Hu, FuLin Zhang, Junlan Feng, Shilei Zhang
Date: 2024-06-19 00:00:00
Tasks: Speaker Recognition, Speaker Verification

Noisy labels are inevitable, even in well-annotated datasets. The detection of noisy labels is of significant importance to enhance the robustness of speaker recognition models. In this paper, we propose a novel noisy label detection approach based on two new statistical metrics: Continuous Inconsistent Counting (CIC) and Total Inconsistent Counting (TIC). These metrics are calculated through Cross-Epoch Counting (CEC) and correspond to the early and late stages of training, respectively. Additionally, we categorize samples based on their prediction results into three categories: inconsistent samples, hard samples, and easy samples. During training, we gradually increase the difficulty of hard samples to update model parameters, preventing noisy labels from being overfitted. Compared to contrastive schemes, our approach not only achieves the best performance in speaker verification but also excels in noisy label detection.
Audio Fingerprinting with Holographic Reduced Representations
Authors: Yusuke Fujita, Tatsuya Komatsu
Date: 2024-06-19 00:00:00
Tasks: Position

This paper proposes an audio fingerprinting model with holographic reduced representation (HRR). The proposed method reduces the number of stored fingerprints, whereas conventional neural audio fingerprinting requires many fingerprints for each audio track to achieve high accuracy and time resolution. We utilize HRR to aggregate multiple fingerprints into a composite fingerprint via circular convolution and summation, resulting in fewer fingerprints with the same dimensional space as the original. Our search method efficiently finds a combined fingerprint in which a query fingerprint exists. Using HRR's inverse operation, it can recover the relative position within a combined fingerprint, retaining the original time resolution. Experiments show that our method can reduce the number of fingerprints with modest accuracy degradation while maintaining the time resolution, outperforming simple decimation and summation-based aggregation methods.
Self-Train Before You Transcribe
Authors: Robert Flynn, Anton Ragni
Date: 2024-06-17 00:00:00
Tasks: Domain Adaptation, Language Modelling, speech-recognition, Speech Recognition, Test-time Adaptation

When there is a mismatch between the training and test domains, current speech recognition systems show significant performance degradation. Self-training methods, such as noisy student teacher training, can help address this and enable the adaptation of models under such domain shifts. However, self-training typically requires a collection of unlabelled target domain data. For settings where this is not practical, we investigate the benefit of performing noisy student teacher training on recordings in the test set as a test-time adaptation approach. Similarly to the dynamic evaluation approach in language modelling, this enables the transfer of information across utterance boundaries and functions as a method of domain adaptation. A range of in-domain and out-of-domain datasets are used for experiments demonstrating large relative gains of up to 32.2%. Interestingly, our method showed larger gains than the typical self-training setup that utilises separate adaptation data.
Automatic Speech Recognition for Biomedical Data in Bengali Language
Authors: Shariar Kabir, Nazmun Nahar, Shyamasree Saha, Mamunur Rashid
Date: 2024-06-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper presents the development of a prototype Automatic Speech Recognition (ASR) system specifically designed for Bengali biomedical data. Recent advancements in Bengali ASR are encouraging, but a lack of domain-specific data limits the creation of practical healthcare ASR models. This project bridges this gap by developing an ASR system tailored for Bengali medical terms like symptoms, severity levels, and diseases, encompassing two major dialects: Bengali and Sylheti. We train and evaluate two popular ASR frameworks on a comprehensive 46-hour Bengali medical corpus. Our core objective is to create deployable health-domain ASR systems for digital health applications, ultimately increasing accessibility for non-technical users in the healthcare sector.