pwc-archive/papers-with-abstracts huggingface dataset:
Model and Evaluation: Towards Fairness in Multilingual Text Classification
Authors: Nankai Lin, Junheng He, Zhenghang Tang, Dong Zhou, Aimin Yang
Date: 2023-03-28 00:00:00
Tasks: Classification, Contrastive Learning, Fairness, Language Modelling, Multilingual text classification, text-classification, Text Classification

Recently, more and more research has focused on addressing bias in text classification models. However, existing research mainly focuses on the fairness of monolingual text classification models, and research on fairness for multilingual text classification is still very limited. In this paper, we focus on the task of multilingual text classification and propose a debiasing framework for multilingual text classification based on contrastive learning. Our proposed method does not rely on any external language resources and can be extended to any other languages. The model contains four modules: multilingual text representation module, language fusion module, text debiasing module, and text classification module. The multilingual text representation module uses a multilingual pre-trained language model to represent the text, the language fusion module makes the semantic spaces of different languages tend to be consistent through contrastive learning, and the text debiasing module uses contrastive learning to make the model unable to identify sensitive attributes' information. The text classification module completes the basic tasks of multilingual text classification. In addition, the existing research on the fairness of multilingual text classification is relatively simple in the evaluation mode. The evaluation method of fairness is the same as the monolingual equality difference evaluation method, that is, the evaluation is performed on a single language. We propose a multi-dimensional fairness evaluation framework for multilingual text classification, which evaluates the model's monolingual equality difference, multilingual equality difference, multilingual equality performance difference, and destructiveness of the fairness strategy. We hope that our work can provide a more general debiasing method and a more comprehensive evaluation framework for multilingual text fairness tasks.
Joint embedding in Hierarchical distance and semantic representation learning for link prediction
Authors: Jin Liu, Jianye Chen, Chongfeng Fan, Fengyu Zhou
Date: 2023-03-28 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Link Prediction, Prediction, Representation Learning, Triplet

The link prediction task aims to predict missing entities or relations in the knowledge graph and is essential for the downstream application. Existing well-known models deal with this task by mainly focusing on representing knowledge graph triplets in the distance space or semantic space. However, they can not fully capture the information of head and tail entities, nor even make good use of hierarchical level information. Thus, in this paper, we propose a novel knowledge graph embedding model for the link prediction task, namely, HIE, which models each triplet (\textit{h}, \textit{r}, \textit{t}) into distance measurement space and semantic measurement space, simultaneously. Moreover, HIE is introduced into hierarchical-aware space to leverage rich hierarchical information of entities and relations for better representation learning. Specifically, we apply distance transformation operation on the head entity in distance space to obtain the tail entity instead of translation-based or rotation-based approaches. Experimental results of HIE on four real-world datasets show that HIE outperforms several existing state-of-the-art knowledge graph embedding methods on the link prediction task and deals with complex relations accurately.
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
Authors: Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, Anna Rumshisky
Date: 2023-03-28 00:00:00
Tasks: Hyperparameter Optimization, parameter-efficient fine-tuning

This paper presents a systematic overview of parameter-efficient fine-tuning methods, covering over 50 papers published between early 2019 and mid-2024. These methods aim to address the challenges of fine-tuning large language models by training only a small subset of parameters. We provide a taxonomy that covers a broad range of methods and present a detailed method comparison with a specific focus on real-life efficiency in fine-tuning multibillion-scale language models. We also conduct an extensive head-to-head experimental comparison of 15 diverse PEFT methods, evaluating their performance and efficiency on models up to 11B parameters. Our findings reveal that methods previously shown to surpass a strong LoRA baseline face difficulties in resource-constrained settings, where hyperparameter optimization is limited and the network is fine-tuned only for a few epochs. Finally, we provide a set of practical recommendations for using PEFT methods and outline potential future research directions.
ChatGPT as a Factual Inconsistency Evaluator for Text Summarization
Authors: Zheheng Luo, Qianqian Xie, Sophia Ananiadou
Date: 2023-03-27 00:00:00
Tasks: Abstractive Text Summarization, Natural Language Inference, Natural Language Understanding, Question Answering, Text Generation, Text Summarization

The performance of text summarization has been greatly boosted by pre-trained language models. A main concern of existing methods is that most generated summaries are not factually inconsistent with their source documents. To alleviate the problem, many efforts have focused on developing effective factuality evaluation metrics based on natural language inference, question answering, and syntactic dependency et al. However, these approaches are limited by either their high computational complexity or the uncertainty introduced by multi-component pipelines, resulting in only partial agreement with human judgement. Most recently, large language models(LLMs) have shown excellent performance in not only text generation but also language comprehension. In this paper, we particularly explore ChatGPT's ability to evaluate factual inconsistency under a zero-shot setting by examining it on both coarse-grained and fine-grained evaluation tasks including binary entailment inference, summary ranking, and consistency rating. Experimental results indicate that ChatGPT generally outperforms previous evaluation metrics across the three tasks, indicating its great potential for factual inconsistency evaluation. However, a closer inspection of ChatGPT's output reveals certain limitations including its preference for more lexically similar candidates, false reasoning, and inadequate understanding of instructions.
Linguistically Informed ChatGPT Prompts to Enhance Japanese-Chinese Machine Translation: A Case Study on Attributive Clauses
Authors: Wenshi Gu
Date: 2023-03-27 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Machine Translation, Translation

In the field of Japanese-Chinese translation linguistics, the issue of correctly translating attributive clauses has persistently proven to be challenging. Present-day machine translation tools often fail to accurately translate attributive clauses from Japanese to Chinese. In light of this, this paper investigates the linguistic problem underlying such difficulties, namely how does the semantic role of the modified noun affect the selection of translation patterns for attributive clauses, from a linguistic perspective. To ad-dress these difficulties, a pre-edit scheme is proposed, which aims to enhance the accuracy of translation. Furthermore, we propose a novel two-step prompt strategy, which combines this pre-edit scheme with ChatGPT, currently the most widely used large language model. This prompt strategy is capable of optimizing translation input in zero-shot scenarios and has been demonstrated to improve the average translation accuracy score by over 35%.
When to be critical? Performance and evolvability in different regimes of neural Ising agents
Authors: Sina Khajehabdollahi, Jan Prosi, Emmanouil Giannakakis, Georg Martius, Anna Levina
Date: 2023-03-28 00:00:00

It has long been hypothesized that operating close to the critical state is beneficial for natural, artificial and their evolutionary systems. We put this hypothesis to test in a system of evolving foraging agents controlled by neural networks that can adapt agents' dynamical regime throughout evolution. Surprisingly, we find that all populations that discover solutions, evolve to be subcritical. By a resilience analysis, we find that there are still benefits of starting the evolution in the critical regime. Namely, initially critical agents maintain their fitness level under environmental changes (for example, in the lifespan) and degrade gracefully when their genome is perturbed. At the same time, initially subcritical agents, even when evolved to the same fitness, are often inadequate to withstand the changes in the lifespan and degrade catastrophically with genetic perturbations. Furthermore, we find the optimal distance to criticality depends on the task complexity. To test it we introduce a hard and simple task: for the hard task, agents evolve closer to criticality whereas more subcritical solutions are found for the simple task. We verify that our results are independent of the selected evolutionary mechanisms by testing them on two principally different approaches: a genetic algorithm and an evolutionary strategy. In summary, our study suggests that although optimal behaviour in the simple task is obtained in a subcritical regime, initializing near criticality is important to be efficient at finding optimal solutions for new tasks of unknown complexity.
Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min--Max Optimization and its Application to Berthing Control Tasks
Authors: Atsuhiro Miyagi, Yoshiki Miyauchi, Atsuo Maki, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
Date: 2023-03-28 00:00:00

In this study, we consider a continuous min--max optimization problem $\min_{x \in \mathbb{X} \max_{y \in \mathbb{Y}}}f(x,y)$ whose objective function is a black-box. We propose a novel approach to minimize the worst-case objective function $F(x) = \max_{y} f(x,y)$ directly using a covariance matrix adaptation evolution strategy (CMA-ES) in which the rankings of solution candidates are approximated by our proposed worst-case ranking approximation (WRA) mechanism. We develop two variants of WRA combined with CMA-ES and approximate gradient ascent as numerical solvers for the inner maximization problem. Numerical experiments show that our proposed approach outperforms several existing approaches when the objective function is a smooth strongly convex--concave function and the interaction between $x$ and $y$ is strong. We investigate the advantages of the proposed approach for problems where the objective function is not limited to smooth strongly convex--concave functions. The effectiveness of the proposed approach is demonstrated in the robust berthing control problem with uncertainty.ngly convex--concave functions. The effectiveness of the proposed approach is demonstrated in the robust berthing control problem with uncertainty.
Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware
Authors: Jakob Kaiser, Raphael Stock, Eric Müller, Johannes Schemmel, Sebastian Schmitt
Date: 2023-03-28 00:00:00

The BrainScaleS-2 (BSS-2) system implements physical models of neurons as well as synapses and aims for an energy-efficient and fast emulation of biological neurons. When replicating neuroscientific experiments on BSS-2, a major challenge is finding suitable model parameters. This study investigates the suitability of the sequential neural posterior estimation (SNPE) algorithm for parameterizing a multi-compartmental neuron model emulated on the BSS-2 analog neuromorphic system. The SNPE algorithm belongs to the class of simulation-based inference methods and estimates the posterior distribution of the model parameters; access to the posterior allows quantifying the confidence in parameter estimations and unveiling correlation between model parameters. For our multi-compartmental model, we show that the approximated posterior agrees with experimental observations and that the identified correlation between parameters fits theoretical expectations. Furthermore, as already shown for software simulations, the algorithm can deal with high-dimensional observations and parameter spaces when the data is generated by emulations on BSS-2. These results suggest that the SNPE algorithm is a promising approach for automating the parameterization and the analyzation of complex models, especially when dealing with characteristic properties of analog neuromorphic substrates, such as trial-to-trial variations or limited parameter ranges.
Modeling and Joint Optimization of Security, Latency, and Computational Cost in Blockchain-based Healthcare Systems
Authors: Zukai Li, Wei Tian, Jingjin Wu
Date: 2023-03-28 00:00:00
Tasks: Management

In the era of the Internet of Things (IoT), blockchain is a promising technology for improving the efficiency of healthcare systems, as it enables secure storage, management, and sharing of real-time health data collected by the IoT devices. As the implementations of blockchain-based healthcare systems usually involve multiple conflicting metrics, it is essential to balance them according to the requirements of specific scenarios. In this paper, we formulate a joint optimization model with three metrics, namely latency, security, and computational cost, that are particularly important for IoT-enabled healthcare. However, it is computationally intractable to identify the exact optimal solution of this problem for practical sized systems. Thus, we propose an algorithm called the Adaptive Discrete Particle Swarm Algorithm (ADPSA) to obtain near-optimal solutions in a low-complexity manner. With its roots in the classical Particle Swarm Optimization (PSO) algorithm, our proposed ADPSA can effectively manage the numerous binary and integer variables in the formulation. We demonstrate by extensive numerical experiments that the ADPSA consistently outperforms existing benchmark approaches, including the original PSO, exhaustive search and Simulated Annealing, in a wide range of scenarios.
The Impact of Asynchrony on Parallel Model-Based EAs
Authors: Arthur Guijt, Dirk Thierens, Tanja Alderliesten, Peter A. N. Bosman
Date: 2023-03-27 00:00:00
Tasks: Evolutionary Algorithms

In a parallel EA one can strictly adhere to the generational clock, and wait for all evaluations in a generation to be done. However, this idle time limits the throughput of the algorithm and wastes computational resources. Alternatively, an EA can be made asynchronous parallel. However, EAs using classic recombination and selection operators (GAs) are known to suffer from an evaluation time bias, which also influences the performance of the approach. Model-Based Evolutionary Algorithms (MBEAs) are more scalable than classic GAs by virtue of capturing the structure of a problem in a model. If this model is learned through linkage learning based on the population, the learned model may also capture biases. Thus, if an asynchronous parallel MBEA is also affected by an evaluation time bias, this could result in learned models to be less suited to solving the problem, reducing performance. Therefore, in this work, we study the impact and presence of evaluation time biases on MBEAs in an asynchronous parallelization setting, and compare this to the biases in GAs. We find that a modern MBEA, GOMEA, is unaffected by evaluation time biases, while the more classical MBEA, ECGA, is affected, much like GAs are.
Evolutionary Design of the Memory Subsystem
Authors: Josefa Díaz Álvarez, José L. Risco-Martín, J. Manuel Colmenar
Date: 2023-03-07 00:00:00
Tasks: Evolutionary Algorithms

The memory hierarchy has a high impact on the performance and power consumption in the system. Moreover, current embedded systems, included in mobile devices, are specifically designed to run multimedia applications, which are memory intensive. This increases the pressure on the memory subsystem and affects the performance and energy consumption. In this regard, the thermal problems, performance degradation and high energy consumption, can cause irreversible damage to the devices. We address the optimization of the whole memory subsystem with three approaches integrated as a single methodology. Firstly, the thermal impact of register file is analyzed and optimized. Secondly, the cache memory is addressed by optimizing cache configuration according to running applications and improving both performance and power consumption. Finally, we simplify the design and evaluation process of general-purpose and customized dynamic memory manager, in the main memory. To this aim, we apply different evolutionary algorithms in combination with memory simulators and profiling tools. This way, we are able to evaluate the quality of each candidate solution and take advantage of the exploration of solutions given by the optimization algorithm.We also provide an experimental experience where our proposal is assessed using well-known benchmark applications.
A source separation approach to temporal graph modelling for computer networks
Authors: Corentin Larroche
Date: 2023-03-28 00:00:00
Tasks: Link Prediction

Detecting malicious activity within an enterprise computer network can be framed as a temporal link prediction task: given a sequence of graphs representing communications between hosts over time, the goal is to predict which edges should--or should not--occur in the future. However, standard temporal link prediction algorithms are ill-suited for computer network monitoring as they do not take account of the peculiar short-term dynamics of computer network activity, which exhibits sharp seasonal variations. In order to build a better model, we propose a source separation-inspired description of computer network activity: at each time step, the observed graph is a mixture of subgraphs representing various sources of activity, and short-term dynamics result from changes in the mixing coefficients. Both qualitative and quantitative experiments demonstrate the validity of our approach.
Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage
Authors: Rafael Alves, Diego S. de Brito, Marcelo C. Medeiros, Ruy M. Ribeiro
Date: 2023-03-22 00:00:00

We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S\&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (e.g., size, value, and profitability) and use sectoral restrictions in the residual covariance matrix. This restricted model is then estimated using vector heterogeneous autoregressive (VHAR) models with the least absolute shrinkage and selection operator (LASSO). Our methodology improves forecasting precision relative to standard benchmarks and leads to better estimates of minimum variance portfolios.
NeuralMind-UNICAMP at 2022 TREC NeuCLIR: Large Boring Rerankers for Cross-lingual Retrieval
Authors: Vitor Jeronymo, Roberto Lotufo, Rodrigo Nogueira
Date: 2023-03-28 00:00:00
Tasks: Cross-Lingual Information Retrieval, Information Retrieval, Retrieval

This paper reports on a study of cross-lingual information retrieval (CLIR) using the mT5-XXL reranker on the NeuCLIR track of TREC 2022. Perhaps the biggest contribution of this study is the finding that despite the mT5 model being fine-tuned only on query-document pairs of the same language it proved to be viable for CLIR tasks, where query-document pairs are in different languages, even in the presence of suboptimal first-stage retrieval performance. The results of the study show outstanding performance across all tasks and languages, leading to a high number of winning positions. Finally, this study provides valuable insights into the use of mT5 in CLIR tasks and highlights its potential as a viable solution. For reproduction refer to https://github.com/unicamp-dl/NeuCLIR22-mT5
Causal Disentangled Recommendation Against User Preference Shifts
Authors: Wenjie Wang, Xinyu Lin, Liuhui Wang, Fuli Feng, Yunshan Ma, Tat-Seng Chua
Date: 2023-03-28 00:00:00
Tasks: Recommendation Systems

Recommender systems easily face the issue of user preference shifts. User representations will become out-of-date and lead to inappropriate recommendations if user preference has shifted over time. To solve the issue, existing work focuses on learning robust representations or predicting the shifting pattern. There lacks a comprehensive view to discover the underlying reasons for user preference shifts. To understand the preference shift, we abstract a causal graph to describe the generation procedure of user interaction sequences. Assuming user preference is stable within a short period, we abstract the interaction sequence as a set of chronological environments. From the causal graph, we find that the changes of some unobserved factors (e.g., becoming pregnant) cause preference shifts between environments. Besides, the fine-grained user preference over categories sparsely affects the interactions with different items. Inspired by the causal graph, our key considerations to handle preference shifts lie in modeling the interaction generation procedure by: 1) capturing the preference shifts across environments for accurate preference prediction, and 2) disentangling the sparse influence from user preference to interactions for accurate effect estimation of preference. To this end, we propose a Causal Disentangled Recommendation (CDR) framework, which captures preference shifts via a temporal variational autoencoder and learns the sparse influence from multiple environments. Specifically, an encoder is adopted to infer the unobserved factors from user interactions while a decoder is to model the interaction generation process. Besides, we introduce two learnable matrices to disentangle the sparse influence from user preference to interactions. Lastly, we devise a multi-objective loss to optimize CDR. Extensive experiments on three datasets show the superiority of CDR.
A comment to "A General Theory of IR Evaluation Measures"
Authors: Fernando Giner
Date: 2023-03-28 00:00:00

The paper "A General Theory of IR Evaluation Measures" develops a formal framework to determine whether IR evaluation measures are interval scales. This comment shows some limitations about its conclusions.
Genetic Analysis of Prostate Cancer with Computer Science Methods
Authors: YuXuan Li, Shi Zhou
Date: 2023-03-28 00:00:00
Tasks: Community Detection, Tumour Classification

Metastatic prostate cancer is one of the most common cancers in men. In the advanced stages of prostate cancer, tumours can metastasise to other tissues in the body, which is fatal. In this thesis, we performed a genetic analysis of prostate cancer tumours at different metastatic sites using data science, machine learning and topological network analysis methods. We presented a general procedure for pre-processing gene expression datasets and pre-filtering significant genes by analytical methods. We then used machine learning models for further key gene filtering and secondary site tumour classification. Finally, we performed gene co-expression network analysis and community detection on samples from different prostate cancer secondary site types. In this work, 13 of the 14,379 genes were selected as the most metastatic prostate cancer related genes, achieving approximately 92% accuracy under cross-validation. In addition, we provide preliminary insights into the co-expression patterns of genes in gene co-expression networks. Project code is available at https://github.com/zcablii/Master_cancer_project.