Reviewerly/exHarmony huggingface dataset:
An LLM can Fool Itself: A Prompt-Based Adversarial Attack

The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM's adversarial robustness. This paper proposes an efficient tool to audit the LLM's adversarial robustness via a prompt-based adversarial attack (PromptAttack). PromptAttack converts adversarial textual attacks into an attack prompt that can cause the victim LLM to output the adversarial sample to fool itself. The attack prompt is composed of three important components: (1) original input (OI) including the original sample and its ground-truth label, (2) attack objective (AO) illustrating a task description of generating a new sample that can fool itself without changing the semantic meaning, and (3) attack guidance (AG) containing the perturbation instructions to guide the LLM on how to complete the task by perturbing the original sample at character, word, and sentence levels, respectively. Besides, we use a fidelity filter to ensure that PromptAttack maintains the original semantic meanings of the adversarial examples. Further, we enhance the attack power of PromptAttack by ensembling adversarial examples at different perturbation levels. Comprehensive empirical results using Llama2 and GPT-3.5 validate that PromptAttack consistently yields a much higher attack success rate compared to AdvGLUE and AdvGLUE++. Interesting findings include that a simple emoji can easily mislead GPT-3.5 to make wrong predictions.
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability

Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence. Predominantly, the likelihood function is only implicitly established by a simulator posing the need for simulation-based inference (SBI). However, the existing algorithms can yield overconfident posteriors (Hermans *et al.*, 2022) defeating the whole purpose of credibility if the uncertainty quantification is inaccurate. We propose to include a calibration term directly into the training objective of the neural model in selected amortized SBI techniques. By introducing a relaxation of the classical formulation of calibration error we enable end-to-end backpropagation. The proposed method is not tied to any particular neural model and brings moderate computational overhead compared to the profits it introduces. It is directly applicable to existing computational pipelines allowing reliable black-box posterior inference. We empirically show on six benchmark problems that the proposed method achieves competitive or better results in terms of coverage and expected posterior density than the previously existing approaches.
Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation

Models for conversational question answering (ConvQA) over knowledge graphs (KGs) are usually trained and tested on benchmarks of gold QA pairs. This implies that training is limited to surface forms seen in the respective datasets, and evaluation is on a small set of held-out questions. Through our proposed framework REIGN, we take several steps to remedy this restricted learning setup. First, we systematically generate reformulations of training questions to increase robustness of models to surface form variations. This is a particularly challenging problem, given the incomplete nature of such questions. Second, we guide ConvQA models towards higher performance by feeding it only those reformulations that help improve their answering quality, using deep reinforcement learning. Third, we demonstrate the viability of training major model components on one benchmark and applying them zero-shot to another. Finally, for a rigorous evaluation of robustness for trained models, we use and release large numbers of diverse reformulations generated by prompting GPT for benchmark test sets (resulting in 20x increase in sizes). Our findings show that ConvQA models with robust training via reformulations, significantly outperform those with standard training from gold QA pairs only.
Y-Diagonal Couplings: Approximating Posteriors with Conditional Wasserstein Distances

In inverse problems, many conditional generative models approximate the posterior measure by minimizing a distance between the joint measure and its learned approximation. While this approach also controls the distance between the posterior measures in the case of the Kullback Leibler divergence, it does not hold true for the Wasserstein distance. We will introduce a conditional Wasserstein distance with a set of restricted couplings that equals the expected Wasserstein distance of the posteriors. By deriving its dual, we find a rigorous way to motivate the loss of conditional Wasserstein GANs. We outline conditions under which the vanilla and the conditional Wasserstein distance coincide. Furthermore, we will show numerical examples where training with the conditional Wasserstein distance yields favorable properties for posterior sampling.
Stable Nonconvex-Nonconcave Training via Linear Interpolation

This paper presents a theoretical analysis of linear interpolation as a principled method for stabilizing (large-scale) neural network training. We argue that instabilities in the optimization process are often caused by the nonmonotonicity of the loss landscape and show how linear interpolation can help by leveraging the theory of nonexpansive operators. We construct a new optimization scheme called relaxed approximate proximal point (RAPP), which is the first explicit method without anchoring to achieve last iterate convergence rates for $\rho$-comonotone problems while only requiring $\rho > -\tfrac{1}{2L}$. The construction extends to constrained and regularized settings. By replacing the inner optimizer in RAPP we rediscover the family of Lookahead algorithms for which we establish convergence in cohypomonotone problems even when the base optimizer is taken to be gradient descent ascent. The range of cohypomonotone problems in which Lookahead converges is further expanded by exploiting that Lookahead inherits the properties of the base optimizer. We corroborate the results with experiments on generative adversarial networks which demonstrates the benefits of the linear interpolation present in both RAPP and Lookahead.
Controlled Randomness Improves the Performance of Transformer Models

During the pre-training step of natural language models, the main objective is to learn a general representation of the pre-training dataset, usually requiring large amounts of textual data to capture the complexity and diversity of natural language. Contrasting this, in most cases, the size of the data available to solve the specific downstream task is often dwarfed by the aforementioned pre-training dataset, especially in domains where data is scarce. We introduce controlled randomness, i.e. noise, into the training process to improve fine-tuning language models and explore the performance of targeted noise in addition to the parameters of these models. We find that adding such noise can improve the performance in our two downstream tasks of joint named entity recognition and relation extraction and text summarization.
Why Can Large Language Models Generate Correct Chain-of-Thoughts?

This paper delves into the capabilities of large language models (LLMs), specifically focusing on advancing the theoretical comprehension of chain-of-thought prompting. We investigate how LLMs can be effectively induced to generate a coherent chain of thoughts. To achieve this, we introduce a two-level hierarchical graphical model tailored for natural language generation. Within this framework, we establish a compelling geometrical convergence rate that gauges the likelihood of an LLM-generated chain of thoughts compared to those originating from the true language. Our findings provide a theoretical justification for the ability of LLMs to produce the correct sequence of thoughts (potentially) explaining performance gains in tasks demanding reasoning skills.
Longer-range Contextualized Masked Autoencoder

Masked image modeling (MIM) has emerged as a promising self-supervised learning (SSL) strategy. The MIM pre-training facilitates learning powerful representations using an encoder-decoder framework by randomly masking some input pixels and reconstructing the masked pixels from the remaining ones. However, as the encoder is trained with partial pixels, the MIM pre-training can suffer from a low capability of understanding long-range dependency. This limitation may hinder its capability to fully understand multiple-range dependencies, resulting in narrow highlighted regions in the attention map that may incur accuracy drops. To mitigate the limitation, We propose a self-supervised learning framework, named Longer-range Contextualized Masked Autoencoder (LC-MAE). LC-MAE effectively leverages a global context understanding of visual representations while simultaneously reducing the spatial redundancy of input at the same time. Our method steers the encoder to learn from entire pixels in multiple views while also learning local representation from sparse pixels. As a result, LC-MAE learns more discriminative representations, leading to a performance improvement of achieving 84.2% top-1 accuracy with ViT-B on ImageNet-1K with 0.6%p gain. We attribute the success to the enhanced pre-training method, as evidenced by the singular value spectrum and attention analyses. Finally, LC-MAE achieves significant performance gains at the downstream semantic segmentation and fine-grained visual classification tasks; and on diverse robust evaluation metrics. Our code will be publicly available.
BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn Dialogues

Interacting with human via high-quality multi-turn dialogues is a key feature of large language models (LLMs). However, human-based evaluation of such capability involves intensive manual labor. This report provides a preliminary evaluation of existing large language models for human-style multi-turn chatting, through an LLM-based approach. We start from real-world human dialogues and keep the very first utterances as the ChatSEED. Then we prompt LLMs to generate a full multi-turn dialogue (tens of utterances) based on the ChatSEED, utterance by utterance. Finally, we adopt state-of-the-art LLMs (GPT-4, \etc) as the judge to evaluate the generated dialogues. With different evaluation protocols, we come to substantially identical conclusions. We find that GPT-4 can generate human-style multi-turn dialogues with impressive quality, significantly outperforms its counterparts. It's difficult for a discriminator to distinguish between GPT-4 generated dialogues and human dialogues. In contrast, other LLMs struggle to generate multi-turn dialogues of satisfactory quality due to poor instruction-following capability, tendency to generate lengthy utterances, or limited general capability. All data and codes will be provided in https://github.com/open-compass/BotChat/ and we hope they can serve as a valuable resource for evaluating multi-turn chatting capabilities of LLMs.
Observation of first- and second-order dissipative phase transitions in a two-photon driven Kerr resonator

In open quantum systems, first- and second-order dissipative phase transitions (DPTs) can emerge in the thermodynamic limit from the competition between unitary evolution, driving terms, and dissipation. The order of a DPT is defined by the continuity properties of the steady state. Until now, second-order DPTs have predominantly been investigated theoretically, while first-order DPTs have been observed in key experiments based on the theory of the single-photon driven Kerr resonator. We present here the first comprehensive experimental and theoretical analysis of both first and second-order DPTs in a two-photon (i.e., parametrically) driven Kerr superconducting resonator. Firstly, we characterize the steady state and its main features at the second- and first-order critical points: squeezing below vacuum and coexistence of two phases with different photon numbers, respectively. Then, by continuously monitoring the system along quantum trajectories, we study the non-equilibrium dynamics across the critical points. We witness the hysteresis cycles associated with the first-order DPT and the spontaneous symmetry breaking due to the second-order DPT. Applying the spectral theory of the Liouvillian superoperator, we develop efficient procedures to quantify the critical slowing down associated with the timescales of these processes. When scaling towards the thermodynamic limit, these timescales span five orders of magnitude. Our results corroborate the predictions derived using the Liouvillian theory of DPTs. This work stands as a compelling example of engineering and controlling of criticality in superconducting circuits. It marks a significant advancement in the use of two-photon driven Kerr resonators for criticality-enhanced quantum information applications.
Application Performance Benchmarks for Quantum Computers

Current technological advancements of quantum computers highlight the need for application-driven, practical and well-defined methods of benchmarking their performance. As the existing NISQ device's quality of two-qubit gate errors rate is even around one percent and the number of qubits is still limited to a few or several dozen, naturally, we need to propose rather small algorithms instances taken from key promising application areas, such as quantum chemistry, combinatorial optimisation or machine learning. While many techniques for assessing the performance of logical components, such as gate fidelity and qubit coherence exist, it is still challenging to extrapolate those values onto the performance of different quantum algorithms and subroutines. This work aims to introduce a series of initial quantum application benchmarks together with a methodology of execution for measuring the performance and fidelity of the results. The proposed suite refers to several variational algorithms widely used on available NISQ devices but also includes examples of quantum circuits designed for a fault-tolerant quantum computer.
Exploring Linguistic Probes for Morphological Generalization

Modern work on the cross-linguistic computational modeling of morphological inflection has typically employed language-independent data splitting algorithms. In this paper, we supplement that approach with language-specific probes designed to test aspects of morphological generalization. Testing these probes on three morphologically distinct languages, English, Spanish, and Swahili, we find evidence that three leading morphological inflection systems employ distinct generalization strategies over conjugational classes and feature sets on both orthographic and phonologically transcribed inputs.
Using Human-like Mechanism to Weaken Effect of Pre-training Weight Bias in Face-Recognition Convolutional Neural Network

Convolutional neural network (CNN), as an important model in artificial intelligence, has been widely used and studied in different disciplines. The computational mechanisms of CNNs are still not fully revealed due to the their complex nature. In this study, we focused on 4 extensively studied CNNs (AlexNet, VGG11, VGG13, and VGG16) which has been analyzed as human-like models by neuroscientists with ample evidence. We trained these CNNs to emotion valence classification task by transfer learning. Comparing their performance with human data, the data unveiled that these CNNs would partly perform as human does. We then update the object-based AlexNet using self-attention mechanism based on neuroscience and behavioral data. The updated FE-AlexNet outperformed all the other tested CNNs and closely resembles human perception. The results further unveil the computational mechanisms of these CNNs. Moreover, this study offers a new paradigm to better understand and improve CNN performance via human data.
4 Augmented reality in cross-domain applications

A comparatively recent but rapidly growing field there at convergence of pattern recognition and visual effects, augmented reality (AR) has a wide range of uses ranging from gameplay and amusement to healthcare and educational settings. Although it has existed for almost half a century, the scientific world has recently shown a substantial amount of interest in it. In this, we focus on the cross-domain applications of AR and efforts are put forward to bring out the key features of the same. Currently, the capability of AR and digital twins (DT) has started to emerge, sparking an increase in ongoing research from both academia and business. Another area of research which is receiving high focus is advanced driver assistance system (ADAS). ADAS are utilized on every level from forecasting the weather to security, regardless of the fact that the machine is operating the vehicle. Such cutting-edge ADASs employ a variety of aids for motorists to ensure the safety of the voyage; such complex aids send out early indications of a variety of situations, including such changes in the road’s state, potential traffic congestion, and severe weather. We also propose a method of AR-assisted ADAS by which the road accidents can be reduced.
Recognizing facial emotion leveraging convolutional neural network

Facial expression recognition technology is a technology based on the combination of artificial intelligence technology and biology, and it is interdisciplinary research. The appearance of this technology shows the diversity of the development direction and application fields of computer technology, but it also means that the development of facial expression recognition technology needs not only the support of computer technology but also the exploration and progress of biology. At present, to recognize facial expressions a system usually contains three stages, namely, face detection stage, feature extraction stage, and face expression recognition stage. The facial expression recognition algorithm is transformed into a classification problem, and a convolutional neural network (CNN) is adopted, where features of the facial expressions could be automatically extracted. It takes as input a preprocessed image and can directly produce encoded features and predictions, leveraging the end-to-end strategy. It could discard the complicated intermediate modeling process of traditional machine learning. In this work a CNN is implemented for the recognition of facial expression on Fer2013 dataset. Moreover, the effectiveness of different numbers of CNN layers are testified on this problem. It could be concluded that with deeper architectures, the CNN tends to perform better.
Research and improvement of movie recommendation based on a collaborative filtering algorithm

Among various recommendation algorithms, collaborative filtering stands out as a popular and effective technique that analyzes user behavior to suggest personalized items. The goal of studying recommendation algorithms is to enhance the exactness of recommendation outcomes and provide users with tailored suggestions. In this paper, the algorithm of collaborative filtering is divided into User-CF and Item-CF, and their algorithm principles and implementation steps are introduced. The similarity calculation of both methods is improved, and a comparison of their advantages and disadvantages is conducted. Experiments are conducted on the MovieLens-latest-small movie dataset to compare the performance of User-CF and Item-CF before and after their similarity calculation is improved. The results show that the similarity improvement of User-CF can substantially enhance the algorithms inclusiveness, while the similarity improvement of Item-CF does not have a considerable impact on the algorithms performance indicators. Finally, the comparison shows that Item-CF has better coverage and popularity, while User-CF has better precision and recall.
The advance of consensus algorithm in blockchain

As a distributed ledger technology, blockchain has found widespread use in a variety of industries, including finance, the Internet of Things (IoT), healthcare, and manufacturing. This technology addresses the trust issue by converting a low-trust centralized ledger into a highly trusted distributed ledger maintained by various entities. Consensus algorithms are one of the fundamental building blocks of the blockchain, controlling how nodes cooperate and synchronize data to perform secure and reliable activities in a decentralized setting. This paper examines the extant mainstream consensus algorithms, introduces six representative consensus algorithms, analyses their benefits and drawbacks, and discusses the application scenarios and suitability of each consensus algorithm in various blockchain platforms.
Research and analysis of matrix multiplica-tion in distributed learning algorithms

Matrix multiplication have become increasingly important nowa-days, which is applied in many kinds of fields. In this case, the need to improve the speed and efficiency of matrix multiplication is in-creasing. In this paper, the author analyzes some relevant theories about matrix multiplication as well as the advantage and disad-vantage of some applications that based on matrix multiplication. It turns out that matrix multiplication has a lot of room for devel-opment in the future cause the current method still has many de-fects and is not perfect. For example, it will still take plenty of time to finish the process of matrix multiplication when the matrices are large in size. However, its gratifying that some improvements have been achieved, which can help to optimize the efficiency. In addi-tion, some advanced methods start to appear. For instance, the method of combining matrix multiplication with AI provides a new direction for future research and development. Consequently, it is predictable that a significant achievement to optimize matrix mul-tiplication will be made in the future.
The Behavior Change of Intelligent Agent Using Finite State Machine in “Save Karang Mumus” Game

Save Karang Mumus is a learning game about caring for the environment. This game is an educational game that can provide knowledge to players so that players are expected to learn while playing. Save Karang Mumus Game uses a role-playing game genre where players must play a role to complete the mission of the game. Multimedia Development Life Cycle development method is used for the development of this game starting from concept, design to distribution. This research focuses on the behavior of NPCs that change based on certain conditions using the Finite State Machine (FSM) method. NPCs will chase players based on the variable amount of trash collected and Health Points. The game is tested using alpha test and beta test. Based on the results, the game can be played well and get a percentage value of 65.66% of the respondents' test results. An application can be said to be good if it is more than 61% and less than 80%. It can be concluded that Save Karang Mumus Game is successful and worthy to be played.