pwc-archive/papers-with-abstracts huggingface dataset:
EmoBench: Evaluating the Emotional Intelligence of Large Language Models
Authors: Sahand Sabour, Siyang Liu, Zheyuan Zhang, June M. Liu, Jinfeng Zhou, Alvionna S. Sunaryo, Juanzi Li, Tatia M. C. Lee, Rada Mihalcea, Minlie Huang
Date: 2024-02-19 00:00:00
Tasks: Emotional Intelligence, Emotion Recognition

Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks. Yet, research on evaluating their Emotional Intelligence (EI) is considerably limited. Existing benchmarks have two major shortcomings: first, they mainly focus on emotion recognition, neglecting essential EI capabilities such as emotion regulation and thought facilitation through emotion understanding; second, they are primarily constructed from existing datasets, which include frequent patterns, explicit information, and annotation errors, leading to unreliable evaluation. We propose EmoBench, a benchmark that draws upon established psychological theories and proposes a comprehensive definition for machine EI, including Emotional Understanding and Emotional Application. EmoBench includes a set of 400 hand-crafted questions in English and Chinese, which are meticulously designed to require thorough reasoning and understanding. Our findings reveal a considerable gap between the EI of existing LLMs and the average human, highlighting a promising direction for future research. Our code and data are publicly available at https://github.com/Sahandfer/EmoBench.
WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More
Authors: Yuxuan Yue, Zhihang Yuan, Haojie Duanmu, Sifan Zhou, Jianlong Wu, Liqiang Nie
Date: 2024-02-19 00:00:00
Tasks: Quantization, Text Generation

Large Language Models (LLMs) face significant deployment challenges due to their substantial memory requirements and the computational demands of auto-regressive text generation process. This paper addresses these challenges by focusing on the quantization of LLMs, a technique that reduces memory consumption by converting model parameters and activations into low-bit integers. We critically analyze the existing quantization approaches, identifying their limitations in balancing the accuracy and efficiency of the quantized LLMs. To advance beyond these limitations, we propose WKVQuant, a PTQ framework especially designed for quantizing weights and the key/value (KV) cache of LLMs. Specifically, we incorporates past-only quantization to improve the computation of attention. Additionally, we introduce two-dimensional quantization strategy to handle the distribution of KV cache, along with a cross-block reconstruction regularization for parameter optimization. Experiments show that WKVQuant achieves almost comparable memory savings to weight-activation quantization, while also approaching the performance of weight-only quantization.
Causal Equal Protection as Algorithmic Fairness
Authors: Marcello Di Bello, Nicolò Cangiotti, Michele Loi
Date: 2024-02-19 00:00:00
Tasks: Classification, Diagnostic, Fairness, Philosophy

By combining the philosophical literature on statistical evidence and the interdisciplinary literature on algorithmic fairness, we revisit recent objections against classification parity in light of causal analyses of algorithmic fairness and the distinction between predictive and diagnostic evidence. We focus on trial proceedings as a black-box classification algorithm in which defendants are sorted into two groups by convicting or acquitting them. We defend a novel principle, causal equal protection, that combines classification parity with the causal approach. In the do-calculus, causal equal protection requires that individuals should not be subject to uneven risks of classification error because of their protected or socially salient characteristics. The explicit use of protected characteristics, however, may be required if it equalizes these risks.
All Language Models Large and Small
Authors: Zhixun Chen, Yali Du, David Mguni
Date: 2024-02-19 00:00:00
Tasks: All, Decision Making, GPU

Many leading language models (LMs) use high-intensity computational resources both during training and execution. This poses the challenge of lowering resource costs for deployment and faster execution of decision-making tasks among others. We introduce a novel plug-and-play LM framework named Language Optimising Network Distribution (LONDI) framework. LONDI learns to selectively employ large LMs only where complex decision-making and reasoning are required while using low-resource LMs (i.e. LMs require less GPU usage, but may not be able to solve the problem alone) everywhere else. LONDI consists of a system of two (off-)policy networks, an LM, a large LM (LLM), and a reinforcement learning module that uses switching controls to quickly learn which system states to call the LLM. We then introduce a variant of LONDI that maintains budget constraints on LLM calls and hence its resource usage. Theoretically, we prove LONDI learns the subset of system states to activate the LLM required to solve the task. We then prove that LONDI converges to optimal solutions while also preserving budgetary constraints on LLM calls almost surely enabling it to solve various tasks while significantly lowering computational costs. We test LONDI's performance in a range of tasks in ScienceWorld and BabyAI-Text and demonstrate that LONDI can solve tasks only solvable by resource-intensive LLMs while reducing GPU usage by up to 30%.
Linear bandits with polylogarithmic minimax regret
Authors: Josep Lumbreras, Marco Tomamichel
Date: 2024-02-19 00:00:00

We study a noise model for linear stochastic bandits for which the subgaussian noise parameter vanishes linearly as we select actions on the unit sphere closer and closer to the unknown vector. We introduce an algorithm for this problem that exhibits a minimax regret scaling as $\log^3(T)$ in the time horizon $T$, in stark contrast the square root scaling of this regret for typical bandit algorithms. Our strategy, based on weighted least-squares estimation, achieves the eigenvalue relation $\lambda_{\min} ( V_t ) = \Omega (\sqrt{\lambda_{\max}(V_t ) })$ for the design matrix $V_t$ at each time step $t$ through geometrical arguments that are independent of the noise model and might be of independent interest. This allows us to tightly control the expected regret in each time step to be of the order $O(\frac1{t})$, leading to the logarithmic scaling of the cumulative regret.
Class-incremental Learning for Time Series: Benchmark and Evaluation
Authors: Zhongzheng Qiao, Quang Pham, Zhen Cao, Hoang H Le, P. N. Suganthan, Xudong Jiang, Ramasamy Savitha
Date: 2024-02-19 00:00:00
Tasks: Activity Recognition, Benchmarking, class-incremental learning, Class Incremental Learning, Human Activity Recognition, Incremental Learning, Time Series, Time Series Classification

Real-world environments are inherently non-stationary, frequently introducing new classes over time. This is especially common in time series classification, such as the emergence of new disease classification in healthcare or the addition of new activities in human activity recognition. In such cases, a learning system is required to assimilate novel classes effectively while avoiding catastrophic forgetting of the old ones, which gives rise to the Class-incremental Learning (CIL) problem. However, despite the encouraging progress in the image and language domains, CIL for time series data remains relatively understudied. Existing studies suffer from inconsistent experimental designs, necessitating a comprehensive evaluation and benchmarking of methods across a wide range of datasets. To this end, we first present an overview of the Time Series Class-incremental Learning (TSCIL) problem, highlight its unique challenges, and cover the advanced methodologies. Further, based on standardized settings, we develop a unified experimental framework that supports the rapid development of new algorithms, easy integration of new datasets, and standardization of the evaluation process. Using this framework, we conduct a comprehensive evaluation of various generic and time-series-specific CIL methods in both standard and privacy-sensitive scenarios. Our extensive experiments not only provide a standard baseline to support future research but also shed light on the impact of various design factors such as normalization layers or memory budget thresholds. Codes are available at https://github.com/zqiao11/TSCIL.
Evaluation of ChatGPT's Smart Contract Auditing Capabilities Based on Chain of Thought
Authors: Yuying Du, Xueyan Tang
Date: 2024-02-19 00:00:00
Tasks: Vulnerability Detection

Smart contracts, as a key component of blockchain technology, play a crucial role in ensuring the automation of transactions and adherence to protocol rules. However, smart contracts are susceptible to security vulnerabilities, which, if exploited, can lead to significant asset losses. This study explores the potential of enhancing smart contract security audits using the GPT-4 model. We utilized a dataset of 35 smart contracts from the SolidiFI-benchmark vulnerability library, containing 732 vulnerabilities, and compared it with five other vulnerability detection tools to evaluate GPT-4's ability to identify seven common types of vulnerabilities. Moreover, we assessed GPT-4's performance in code parsing and vulnerability capture by simulating a professional auditor's auditing process using CoT(Chain of Thought) prompts based on the audit reports of eight groups of smart contracts. We also evaluated GPT-4's ability to write Solidity Proof of Concepts (PoCs). Through experimentation, we found that GPT-4 performed poorly in detecting smart contract vulnerabilities, with a high Precision of 96.6%, but a low Recall of 37.8%, and an F1-score of 41.1%, indicating a tendency to miss vulnerabilities during detection. Meanwhile, it demonstrated good contract code parsing capabilities, with an average comprehensive score of 6.5, capable of identifying the background information and functional relationships of smart contracts; in 60% of the cases, it could write usable PoCs, suggesting GPT-4 has significant potential application in PoC writing. These experimental results indicate that GPT-4 lacks the ability to detect smart contract vulnerabilities effectively, but its performance in contract code parsing and PoC writing demonstrates its significant potential as an auxiliary tool in enhancing the efficiency and effectiveness of smart contract security audits.
Training Green AI Models Using Elite Samples
Authors: Mohammed Alswaitti, Roberto Verdecchia, Grégoire Danoy, Pascal Bouvry, Johnatan Pecero
Date: 2024-02-19 00:00:00

The substantial increase in AI model training has considerable environmental implications, mandating more energy-efficient and sustainable AI practices. On the one hand, data-centric approaches show great potential towards training energy-efficient AI models. On the other hand, instance selection methods demonstrate the capability of training AI models with minimised training sets and negligible performance degradation. Despite the growing interest in both topics, the impact of data-centric training set selection on energy efficiency remains to date unexplored. This paper presents an evolutionary-based sampling framework aimed at (i) identifying elite training samples tailored for datasets and model pairs, (ii) comparing model performance and energy efficiency gains against typical model training practice, and (iii) investigating the feasibility of this framework for fostering sustainable model training practices. To evaluate the proposed framework, we conducted an empirical experiment including 8 commonly used AI classification models and 25 publicly available datasets. The results showcase that by considering 10% elite training samples, the models' performance can show a 50% improvement and remarkable energy savings of 98% compared to the common training practice.
Cluster Metric Sensitivity to Irrelevant Features
Authors: Miles McCrory, Spencer A. Thomas
Date: 2024-02-19 00:00:00
Tasks: Clustering, feature selection, Sensitivity

Clustering algorithms are used extensively in data analysis for data exploration and discovery. Technological advancements lead to continually growth of data in terms of volume, dimensionality and complexity. This provides great opportunities in data analytics as the data can be interrogated for many different purposes. This however leads challenges, such as identification of relevant features for a given task. In supervised tasks, one can utilise a number of methods to optimise the input features for the task objective (e.g. classification accuracy). In unsupervised problems, such tools are not readily available, in part due to an inability to quantify feature relevance in unlabeled tasks. In this paper, we investigate the sensitivity of clustering performance noisy uncorrelated variables iteratively added to baseline datasets with well defined clusters. We show how different types of irrelevant variables can impact the outcome of a clustering result from $k$-means in different ways. We observe a resilience to very high proportions of irrelevant features for adjusted rand index (ARI) and normalised mutual information (NMI) when the irrelevant features are Gaussian distributed. For Uniformly distributed irrelevant features, we notice the resilience of ARI and NMI is dependent on the dimensionality of the data and exhibits tipping points between high scores and near zero. Our results show that the Silhouette Coefficient and the Davies-Bouldin score are the most sensitive to irrelevant added features exhibiting large changes in score for comparably low proportions of irrelevant features regardless of underlying distribution or data scaling. As such the Silhouette Coefficient and the Davies-Bouldin score are good candidates for optimising feature selection in unsupervised clustering tasks.
A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions
Authors: Xiaxia Wang, Gong Cheng
Date: 2024-02-19 00:00:00
Tasks: Knowledge Graphs, Survey

With the continuous growth of large Knowledge Graphs (KGs), extractive KG summarization becomes a trending task. Aiming at distilling a compact subgraph with condensed information, it facilitates various downstream KG-based tasks. In this survey paper, we are among the first to provide a systematic overview of its applications and define a taxonomy for existing methods from its interdisciplinary studies. Future directions are also laid out based on our extensive and comparative review.
Remember This Event That Year? Assessing Temporal Information and Reasoning in Large Language Models
Authors: Himanshu Beniwal, Dishant Patel, Kowsik Nandagopan D, Hritik Ladia, Ankit Yadav, Mayank Singh
Date: 2024-02-19 00:00:00

Large Language Models (LLMs) are increasingly ubiquitous, yet their ability to retain and reason about temporal information remains limited, hindering their application in real-world scenarios where understanding the sequential nature of events is crucial. Our study experiments with 12 state-of-the-art models (ranging from 2B to 70B+ parameters) on a novel numerical-temporal dataset, \textbf{TempUN}, spanning from 10,000 BCE to 2100 CE, to uncover significant temporal retention and comprehension limitations. We propose six metrics to assess three learning paradigms to enhance temporal knowledge acquisition. Our findings reveal that open-source models exhibit knowledge gaps more frequently, suggesting a trade-off between limited knowledge and incorrect responses. Additionally, various fine-tuning approaches significantly improved performance, reducing incorrect outputs and impacting the identification of 'information not available' in the generations. The associated dataset and code are available at (https://github.com/lingoiitgn/TempUN).
Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks
Authors: Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Zhouchen Lin
Date: 2024-02-19 00:00:00
Tasks: Continual Learning

Neuromorphic computing with spiking neural networks is promising for energy-efficient artificial intelligence (AI) applications. However, different from humans who continually learn different tasks in a lifetime, neural network models suffer from catastrophic forgetting. How could neuronal operations solve this problem is an important question for AI and neuroscience. Many previous studies draw inspiration from observed neuroscience phenomena and propose episodic replay or synaptic metaplasticity, but they are not guaranteed to explicitly preserve knowledge for neuron populations. Other works focus on machine learning methods with more mathematical grounding, e.g., orthogonal projection on high dimensional spaces, but there is no neural correspondence for neuromorphic computing. In this work, we develop a new method with neuronal operations based on lateral connections and Hebbian learning, which can protect knowledge by projecting activity traces of neurons into an orthogonal subspace so that synaptic weight update will not interfere with old tasks. We show that Hebbian and anti-Hebbian learning on recurrent lateral connections can effectively extract the principal subspace of neural activities and enable orthogonal projection. This provides new insights into how neural circuits and Hebbian learning can help continual learning, and also how the concept of orthogonal projection can be realized in neuronal systems. Our method is also flexible to utilize arbitrary training methods based on presynaptic activities/traces. Experiments show that our method consistently solves forgetting for spiking neural networks with nearly zero forgetting under various supervised training methods with different error propagation approaches, and outperforms previous approaches under various settings. Our method can pave a solid path for building continual neuromorphic computing systems.
Imbalance in Regression Datasets
Authors: Daniel Kowatsch, Nicolas M. Müller, Kilian Tscharke, Philip Sperl, Konstantin Bötinger
Date: 2024-02-19 00:00:00
Tasks: regression

For classification, the problem of class imbalance is well known and has been extensively studied. In this paper, we argue that imbalance in regression is an equally important problem which has so far been overlooked: Due to under- and over-representations in a data set's target distribution, regressors are prone to degenerate to naive models, systematically neglecting uncommon training data and over-representing targets seen often during training. We analyse this problem theoretically and use resulting insights to develop a first definition of imbalance in regression, which we show to be a generalisation of the commonly employed imbalance measure in classification. With this, we hope to turn the spotlight on the overlooked problem of imbalance in regression and to provide common ground for future research.
Analysis of Multidomain Abstractive Summarization Using Salience Allocation
Authors: Tohida Rehman, Raghubir Bose, Soumik Dey, Samiran Chattopadhyay
Date: 2024-02-19 00:00:00
Tasks: Abstractive Text Summarization, Articles, Event-Driven Trading, Text Summarization

This paper explores the realm of abstractive text summarization through the lens of the SEASON (Salience Allocation as Guidance for Abstractive SummarizatiON) technique, a model designed to enhance summarization by leveraging salience allocation techniques. The study evaluates SEASON's efficacy by comparing it with prominent models like BART, PEGASUS, and ProphetNet, all fine-tuned for various text summarization tasks. The assessment is conducted using diverse datasets including CNN/Dailymail, SAMSum, and Financial-news based Event-Driven Trading (EDT), with a specific focus on a financial dataset containing a substantial volume of news articles from 2020/03/01 to 2021/05/06. This paper employs various evaluation metrics such as ROUGE, METEOR, BERTScore, and MoverScore to evaluate the performance of these models fine-tuned for generating abstractive summaries. The analysis of these metrics offers a thorough insight into the strengths and weaknesses demonstrated by each model in summarizing news dataset, dialogue dataset and financial text dataset. The results presented in this paper not only contribute to the evaluation of the SEASON model's effectiveness but also illuminate the intricacies of salience allocation techniques across various types of datasets.
Mini-Hes: A Parallelizable Second-order Latent Factor Analysis Model
Authors: Jialiang Wang, Weiling Li, Yurong Zhong, Xin Luo
Date: 2024-02-19 00:00:00
Tasks: Recommendation Systems

Interactions among large number of entities is naturally high-dimensional and incomplete (HDI) in many big data related tasks. Behavioral characteristics of users are hidden in these interactions, hence, effective representation of the HDI data is a fundamental task for understanding user behaviors. Latent factor analysis (LFA) model has proven to be effective in representing HDI data. The performance of an LFA model relies heavily on its training process, which is a non-convex optimization. It has been proven that incorporating local curvature and preprocessing gradients during its training process can lead to superior performance compared to LFA models built with first-order family methods. However, with the escalation of data volume, the feasibility of second-order algorithms encounters challenges. To address this pivotal issue, this paper proposes a mini-block diagonal hessian-free (Mini-Hes) optimization for building an LFA model. It leverages the dominant diagonal blocks in the generalized Gauss-Newton matrix based on the analysis of the Hessian matrix of LFA model and serves as an intermediary strategy bridging the gap between first-order and second-order optimization methods. Experiment results indicate that, with Mini-Hes, the LFA model outperforms several state-of-the-art models in addressing missing data estimation task on multiple real HDI datasets from recommender system. (The source code of Mini-Hes is available at https://github.com/Goallow/Mini-Hes)
Team QUST at SemEval-2024 Task 8: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting AI-generated Text
Authors: Xiaoman Xu, Xiangrun Li, Taihang Wang, Jianxiang Tian, Ye Jiang
Date: 2024-02-19 00:00:00
Tasks: Data Augmentation

This paper presents the participation of team QUST in Task 8 SemEval 2024. We first performed data augmentation and cleaning on the dataset to enhance model training efficiency and accuracy. In the monolingual task, we evaluated traditional deep-learning methods, multiscale positive-unlabeled framework (MPU), fine-tuning, adapters and ensemble methods. Then, we selected the top-performing models based on their accuracy from the monolingual models and evaluated them in subtasks A and B. The final model construction employed a stacking ensemble that combined fine-tuning with MPU. Our system achieved 8th (scored 8th in terms of accuracy, officially ranked 13th) place in the official test set in multilingual settings of subtask A. We release our system code at:https://github.com/warmth27/SemEval2024_QUST