pwc-archive/papers-with-abstracts huggingface dataset:
Investigating Fouling Efficiency in Football Using Expected Booking (xB) Model
Authors: Adnan Azmat, Su Su Yi
Date: 2024-01-16 00:00:00

This paper introduces the Expected Booking (xB) model, a novel metric designed to estimate the likelihood of a foul resulting in a yellow card in football. Through three iterative experiments, employing ensemble methods, the model demonstrates improved performance with additional features and an expanded dataset. Analysis of FIFA World Cup 2022 data validates the model's efficacy in providing insights into team and player fouling tactics, aligning with actual defensive performance. The xB model addresses a gap in fouling efficiency examination, emphasizing defensive strategies which often overlooked. Further enhancements are suggested through the incorporation of comprehensive data and spatial features.
Survival Analysis of Young Triple-Negative Breast Cancer Patients
Authors: M. Mehdi Owrang O, Fariba Jafari Horestani, Ginger Schwarz
Date: 2024-01-15 00:00:00
Tasks: Prognosis, Survival Analysis

Breast cancer prognosis is crucial for effective treatment, with the disease more common in women over 40 years old but rare under 40 years old, where less than 5 percent of cases occur in the U.S. Studies indicate a worse prognosis in younger women, which varies by ethnicity. Breast cancers are classified based on receptors like estrogen, progesterone, and HER2. Triple-negative breast cancer (TNBC), lacking these receptors, accounts for about 15 percent of cases and is more prevalent in younger patients, often resulting in poorer outcomes. Nevertheless, the impact of age on TNBC prognosis remains unclear. Factors like age, race, tumor grade, size, and lymph node status are studied for their role in TNBC's clinical outcomes, but current research is inconclusive about age-related differences. This study uses SEER data set to examine the influence of younger age on survivability in TNBC patients, aiming to determine if age is a significant prognostic factor. Our experimental results on SEER dataset confirm the existing research reports that TNBC patients have worse prognosis compared to non-TNBC based on age. Our main goal was to investigate whether younger age has any significance on the survivability of TNBC patients. Experimental results do not show that younger age has any significance on the prognosis and survival rate of the TNBC patients
Decoupled Prototype Learning for Reliable Test-Time Adaptation
Authors: Guowei Wang, Changxing Ding, Wentao Tan, Mingkui Tan
Date: 2024-01-15 00:00:00
Tasks: Domain Generalization, Test-time Adaptation

Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. However, its performance is significantly affected by noisy pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss's vulnerability to label noise. To address this issue, we propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation. First, we decouple the optimization of class prototypes. For each class prototype, we reduce its distance with positive samples and enlarge its distance with negative samples in a contrastive manner. This strategy prevents the model from overfitting to noisy pseudo-labels. Second, we propose a memory-based strategy to enhance DPL's robustness for the small batch sizes often encountered in TTA. We update each class's pseudo-feature from a memory in a momentum manner and insert an additional DPL loss. Finally, we introduce a consistency regularization-based approach to leverage samples with unconfident pseudo-labels. This approach transfers feature styles of samples with unconfident pseudo-labels to those with confident pseudo-labels. Thus, more reliable samples for TTA are created. The experimental results demonstrate that our methods achieve state-of-the-art performance on domain generalization benchmarks, and reliably improve the performance of self-training-based methods on image corruption benchmarks. The code will be released.
Do We Really Even Need Data?
Authors: Kentaro Hoffman, Stephen Salerno, Awan Afiaz, Jeffrey T. Leek, Tyler H. McCormick
Date: 2024-01-14 00:00:00
Tasks: Survey Sampling

As artificial intelligence and machine learning tools become more accessible, and scientists face new obstacles to data collection (e.g. rising costs, declining survey response rates), researchers increasingly use predictions from pre-trained algorithms as outcome variables. Though appealing for financial and logistical reasons, using standard tools for inference can misrepresent the association between independent variables and the outcome of interest when the true, unobserved outcome is replaced by a predicted value. In this paper, we characterize the statistical challenges inherent to this so-called ``inference with predicted data'' problem and elucidate three potential sources of error: (i) the relationship between predicted outcomes and their true, unobserved counterparts, (ii) robustness of the machine learning model to resampling or uncertainty about the training data, and (iii) appropriately propagating not just bias but also uncertainty from predictions into the ultimate inference procedure.
Computationally Efficient Optimisation of Elbow-Type Draft Tube Using Neural Network Surrogates
Authors: Ante Sikirica, Ivana Lučin, Marta Alvir, Lado Kranjčević, Zoran Čarija
Date: 2024-01-14 00:00:00

This study aims to provide a comprehensive assessment of single-objective and multi-objective optimisation algorithms for the design of an elbow-type draft tube, as well as to introduce a computationally efficient optimisation workflow. The proposed workflow leverages deep neural network surrogates trained on data obtained from numerical simulations. The use of surrogates allows for a more flexible and faster evaluation of novel designs. The success history-based adaptive differential evolution with linear reduction and the multi-objective evolutionary algorithm based on decomposition were identified as the best-performing algorithms and used to determine the influence of different objectives in the single-objective optimisation and their combined impact on the draft tube design in the multi-objective optimisation. The results for the single-objective algorithm are consistent with those of the multi-objective algorithm when the objectives are considered separately. Multi-objective approach, however, should typically be chosen, especially for computationally inexpensive surrogates. A multi-criteria decision analysis method was used to obtain optimal multi-objective results, showing an improvement of 1.5% and 17% for the pressure recovery factor and drag coefficient, respectively. The difference between the predictions and the numerical results is less than 0.5% for the pressure recovery factor and 3% for the drag coefficient. As the demand for renewable energy continues to increase, the relevance of data-driven optimisation workflows, as discussed in this study, will become increasingly important, especially in the context of global sustainability efforts.
Evaluating LLMs' Mathematical and Coding Competency through Ontology-guided Interventions
Authors: Pengfei Hong, Navonil Majumder, Deepanway Ghosal, Somak Aditya, Rada Mihalcea, Soujanya Poria
Date: 2024-01-17 00:00:00
Tasks: Arithmetic Reasoning, Code Generation, GSM8K, HumanEval, Logical Reasoning, Math, Mathematical Reasoning

Recent advancements in Large Language Models (LLMs) have showcased striking results on existing logical reasoning benchmarks, with some models even surpassing human performance. However, the true depth of their competencies and robustness in reasoning tasks remains an open question. To this end, in this paper, we focus on two popular reasoning tasks: arithmetic reasoning and code generation. Particularly, we introduce (i) a general ontology of perturbations for math and coding questions, (ii) a semi-automatic method to apply these perturbations, and (iii) two datasets, GSMORE and HUMANEVAL-CORE, respectively, of perturbed math and coding problems to probe LLM capabilities in numeric reasoning and coding tasks. Through comprehensive evaluations of both closed-source and open-source LLMs, we show a significant performance drop across all the models against the perturbed questions, suggesting that the current LLMs lack robust problem solving skills and structured reasoning abilities in many areas, as defined by our ontology. We open-source the datasets and source codes at: https://github.com/declare-lab/LLM-ReasoningTest.
Efficient slot labelling
Authors: Vladimir Vlasov
Date: 2024-01-17 00:00:00

Slot labelling is an essential component of any dialogue system, aiming to find important arguments in every user turn. Common approaches involve large pre-trained language models (PLMs) like BERT or RoBERTa, but they face challenges such as high computational requirements and dependence on pre-training data. In this work, we propose a lightweight method which performs on par or better than the state-of-the-art PLM-based methods, while having almost 10x less trainable parameters. This makes it especially applicable for real-life industry scenarios.
Learning from Implicit User Feedback, Emotions and Demographic Information in Task-Oriented and Document-Grounded Dialogues
Authors: Dominic Petrak, Thy Thy Tran, Iryna Gurevych
Date: 2024-01-17 00:00:00

Implicit user feedback, user emotions and demographic information have shown to be promising sources for improving the accuracy and user engagement of responses generated by dialogue systems. However, the influence of such information on task completion and factual consistency, which are important criteria for task-oriented and document-grounded dialogues, is not yet known. To address this, we introduce FEDI, the first English task-oriented and document-grounded dialogue dataset annotated with this information. Our experiments with Flan-T5, GPT-2 and Llama 2 show a particularly positive impact on task completion and factual consistency. Participants in our human evaluation reported that the responses generated by the feedback-trained models were more informative (Flan-T5 and GPT-2), relevant and factual consistent (Llama 2).
Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges
Authors: Aiqi Jiang, Arkaitz Zubiaga
Date: 2024-01-17 00:00:00
Tasks: Cross-Lingual Transfer, Survey, Transfer Learning

The growing prevalence and rapid evolution of offensive language in social media amplify the complexities of detection, particularly highlighting the challenges in identifying such content across diverse languages. This survey presents a systematic and comprehensive exploration of Cross-Lingual Transfer Learning (CLTL) techniques in offensive language detection in social media. Our study stands as the first holistic overview to focus exclusively on the cross-lingual scenario in this domain. We analyse 67 relevant papers and categorise these studies across various dimensions, including the characteristics of multilingual datasets used, the cross-lingual resources employed, and the specific CLTL strategies implemented. According to "what to transfer", we also summarise three main CLTL transfer approaches: instance, feature, and parameter transfer. Additionally, we shed light on the current challenges and future research opportunities in this field. Furthermore, we have made our survey resources available online, including two comprehensive tables that provide accessible references to the multilingual datasets and CLTL methods used in the reviewed literature.
UniVIE: A Unified Label Space Approach to Visual Information Extraction from Form-like Documents
Authors: Kai Hu, Jiawei Wang, WeiHong Lin, Zhuoyao Zhong, Lei Sun, Qiang Huo
Date: 2024-01-17 00:00:00
Tasks: Decoder, Form, Key Information Extraction, Key-value Pair Extraction, Relation, Relation Prediction

Existing methods for Visual Information Extraction (VIE) from form-like documents typically fragment the process into separate subtasks, such as key information extraction, key-value pair extraction, and choice group extraction. However, these approaches often overlook the hierarchical structure of form documents, including hierarchical key-value pairs and hierarchical choice groups. To address these limitations, we present a new perspective, reframing VIE as a relation prediction problem and unifying labels of different tasks into a single label space. This unified approach allows for the definition of various relation types and effectively tackles hierarchical relationships in form-like documents. In line with this perspective, we present UniVIE, a unified model that addresses the VIE problem comprehensively. UniVIE functions using a coarse-to-fine strategy. It initially generates tree proposals through a tree proposal network, which are subsequently refined into hierarchical trees by a relation decoder module. To enhance the relation prediction capabilities of UniVIE, we incorporate two novel tree constraints into the relation decoder: a tree attention mask and a tree level embedding. Extensive experimental evaluations on both our in-house dataset HierForms and a publicly available dataset SIBR, substantiate that our method achieves state-of-the-art results, underscoring the effectiveness and potential of our unified approach in advancing the field of VIE.
QAnswer: Towards Question Answering Search over Websites
Authors: Kunpeng Guo, Clement Defretiere, Dennis Diefenbach, Christophe Gravier, Antoine Gourru
Date: 2024-01-17 00:00:00
Tasks: Knowledge Graphs, Question Answering

Question Answering (QA) is increasingly used by search engines to provide results to their end-users, yet very few websites currently use QA technologies for their search functionality. To illustrate the potential of QA technologies for the website search practitioner, we demonstrate web searches that combine QA over knowledge graphs and QA over free text -- each being usually tackled separately. We also discuss the different benefits and drawbacks of both approaches for web site searches. We use the case studies made of websites hosted by the Wikimedia Foundation (namely Wikipedia and Wikidata). Differently from a search engine (e.g. Google, Bing, etc), the data are indexed integrally, i.e. we do not index only a subset, and they are indexed exclusively, i.e. we index only data available on the corresponding website.
Fine-tuning Strategies for Domain Specific Question Answering under Low Annotation Budget Constraints
Authors: Kunpeng Guo, Dennis Diefenbach, Antoine Gourru, Christophe Gravier
Date: 2024-01-17 00:00:00
Tasks: Language Modeling, Language Modelling, Question Answering

The progress introduced by pre-trained language models and their fine-tuning has resulted in significant improvements in most downstream NLP tasks. The unsupervised training of a language model combined with further target task fine-tuning has become the standard QA fine-tuning procedure. In this work, we demonstrate that this strategy is sub-optimal for fine-tuning QA models, especially under a low QA annotation budget, which is a usual setting in practice due to the extractive QA labeling cost. We draw our conclusions by conducting an exhaustive analysis of the performance of the alternatives of the sequential fine-tuning strategy on different QA datasets. Based on the experiments performed, we observed that the best strategy to fine-tune the QA model in low-budget settings is taking a pre-trained language model (PLM) and then fine-tuning PLM with a dataset composed of the target dataset and SQuAD dataset. With zero extra annotation effort, the best strategy outperforms the standard strategy by 2.28% to 6.48%. Our experiments provide one of the first investigations on how to best fine-tune a QA system under a low budget and are therefore of the utmost practical interest to the QA practitioners.
Bridging Research and Readers: A Multi-Modal Automated Academic Papers Interpretation System
Authors: Feng Jiang, Kuang Wang, Haizhou Li
Date: 2024-01-17 00:00:00

In the contemporary information era, significantly accelerated by the advent of Large-scale Language Models, the proliferation of scientific literature is reaching unprecedented levels. Researchers urgently require efficient tools for reading and summarizing academic papers, uncovering significant scientific literature, and employing diverse interpretative methodologies. To address this burgeoning demand, the role of automated scientific literature interpretation systems has become paramount. However, prevailing models, both commercial and open-source, confront notable challenges: they often overlook multimodal data, grapple with summarizing over-length texts, and lack diverse user interfaces. In response, we introduce an open-source multi-modal automated academic paper interpretation system (MMAPIS) with three-step process stages, incorporating LLMs to augment its functionality. Our system first employs the hybrid modality preprocessing and alignment module to extract plain text, and tables or figures from documents separately. It then aligns this information based on the section names they belong to, ensuring that data with identical section names are categorized under the same section. Following this, we introduce a hierarchical discourse-aware summarization method. It utilizes the extracted section names to divide the article into shorter text segments, facilitating specific summarizations both within and between sections via LLMs with specific prompts. Finally, we have designed four types of diversified user interfaces, including paper recommendation, multimodal Q\&A, audio broadcasting, and interpretation blog, which can be widely applied across various scenarios. Our qualitative and quantitative evaluations underscore the system's superiority, especially in scientific summarization, where it outperforms solutions relying solely on GPT-4.
Textual Summarisation of Large Sets: Towards a General Approach
Authors: Kittipitch Kuptavanich, Ehud Reiter, Kees Van Deemter, Advaith Siddharthan
Date: 2024-01-17 00:00:00

We are developing techniques to generate summary descriptions of sets of objects. In this paper, we present and evaluate a rule-based NLG technique for summarising sets of bibliographical references in academic papers. This extends our previous work on summarising sets of consumer products and shows how our model generalises across these two very different domains.
Explain Thyself Bully: Sentiment Aided Cyberbullying Detection with Explanation
Authors: Krishanu Maity, Prince Jha, Raghav Jain, Sriparna Saha, Pushpak Bhattacharyya
Date: 2024-01-17 00:00:00
Tasks: Sentence, Sentiment Analysis

Cyberbullying has become a big issue with the popularity of different social media networks and online communication apps. While plenty of research is going on to develop better models for cyberbullying detection in monolingual language, there is very little research on the code-mixed languages and explainability aspect of cyberbullying. Recent laws like "right to explanations" of General Data Protection Regulation, have spurred research in developing interpretable models rather than focusing on performance. Motivated by this we develop the first interpretable multi-task model called {\em mExCB} for automatic cyberbullying detection from code-mixed languages which can simultaneously solve several tasks, cyberbullying detection, explanation/rationale identification, target group detection and sentiment analysis. We have introduced {\em BullyExplain}, the first benchmark dataset for explainable cyberbullying detection in code-mixed language. Each post in {\em BullyExplain} dataset is annotated with four labels, i.e., {\em bully label, sentiment label, target and rationales (explainability)}, i.e., which phrases are being responsible for annotating the post as a bully. The proposed multitask framework (mExCB) based on CNN and GRU with word and sub-sentence (SS) level attention is able to outperform several baselines and state of the art models when applied on {\em BullyExplain} dataset.
AttackEval: How to Evaluate the Effectiveness of Jailbreak Attacking on Large Language Models
Authors: Dong Shu, Mingyu Jin, Chong Zhang, Liangyao Li, ZiHao Zhou, Yongfeng Zhang
Date: 2024-01-17 00:00:00

Ensuring the security of large language models (LLMs) against attacks has become increasingly urgent, with jailbreak attacks representing one of the most sophisticated threats. To deal with such risks, we introduce an innovative framework that can help evaluate the effectiveness of jailbreak attacks on LLMs. Unlike traditional binary evaluations focusing solely on the robustness of LLMs, our method assesses the effectiveness of the attacking prompts themselves. We present two distinct evaluation frameworks: a coarse-grained evaluation and a fine-grained evaluation. Each framework uses a scoring range from 0 to 1, offering unique perspectives and allowing for the assessment of attack effectiveness in different scenarios. Additionally, we develop a comprehensive ground truth dataset specifically tailored for jailbreak prompts. This dataset serves as a crucial benchmark for our current study and provides a foundational resource for future research. By comparing with traditional evaluation methods, our study shows that the current results align with baseline metrics while offering a more nuanced and fine-grained assessment. It also helps identify potentially harmful attack prompts that might appear harmless in traditional evaluations. Overall, our work establishes a solid foundation for assessing a broader range of attack prompts in the area of prompt injection.
ReFT: Reasoning with Reinforced Fine-Tuning
Authors: Trung Quoc Luong, Xinbo Zhang, Zhanming Jie, Peng Sun, Xiaoran Jin, Hang Li
Date: 2024-01-17 00:00:00
Tasks: GSM8K, Math, Re-Ranking

One way to enhance the reasoning capability of Large Language Models (LLMs) is to conduct Supervised Fine-Tuning (SFT) using Chain-of-Thought (CoT) annotations. This approach does not show sufficiently strong generalization ability, however, because the training only relies on the given CoT data. In math problem-solving, for example, there is usually only one annotated reasoning path for each question in the training data. Intuitively, it would be better for the algorithm to learn from multiple annotated reasoning paths given a question. To address this issue, we propose a simple yet effective approach called Reinforced Fine-Tuning (ReFT) to enhance the generalizability of learning LLMs for reasoning, with math problem-solving as an example. ReFT first warmups the model with SFT, and then employs on-line reinforcement learning, specifically the PPO algorithm in this paper, to further fine-tune the model, where an abundance of reasoning paths are automatically sampled given the question and the rewards are naturally derived from the ground-truth answers. Extensive experiments on GSM8K, MathQA, and SVAMP datasets show that ReFT significantly outperforms SFT, and the performance can be potentially further boosted by combining inference-time strategies such as majority voting and re-ranking. Note that ReFT obtains the improvement by learning from the same training questions as SFT, without relying on extra or augmented training questions. This indicates a superior generalization ability for ReFT.