pwc-archive/papers-with-abstracts huggingface dataset:
From Wide to Deep: Dimension Lifting Network for Parameter-efficient Knowledge Graph Embedding
Authors: Borui Cai, Yong Xiang, Longxiang Gao, Di wu, He Zhang, Jiong Jin, Tom Luan
Date: 2023-03-22 00:00:00
Tasks: Graph Embedding, Knowledge Distillation, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction

Knowledge graph embedding (KGE) that maps entities and relations into vector representations is essential for downstream applications. Conventional KGE methods require high-dimensional representations to learn the complex structure of knowledge graph, but lead to oversized model parameters. Recent advances reduce parameters by low-dimensional entity representations, while developing techniques (e.g., knowledge distillation or reinvented representation forms) to compensate for reduced dimension. However, such operations introduce complicated computations and model designs that may not benefit large knowledge graphs. To seek a simple strategy to improve the parameter efficiency of conventional KGE models, we take inspiration from that deeper neural networks require exponentially fewer parameters to achieve expressiveness comparable to wider networks for compositional structures. We view all entity representations as a single-layer embedding network, and conventional KGE methods that adopt high-dimensional entity representations equal widening the embedding network to gain expressiveness. To achieve parameter efficiency, we instead propose a deeper embedding network for entity representations, i.e., a narrow entity embedding layer plus a multi-layer dimension lifting network (LiftNet). Experiments on three public datasets show that by integrating LiftNet, four conventional KGE methods with 16-dimensional representations achieve comparable link prediction accuracy as original models that adopt 512-dimensional representations, saving 68.4% to 96.9% parameters.
SignCRF: Scalable Channel-agnostic Data-driven Radio Authentication System
Authors: Amani Al-shawabka, Philip Pietraski, Sudhir B Pattar, Pedram Johari, Tommaso Melodia
Date: 2023-03-21 00:00:00

Radio Frequency Fingerprinting through Deep Learning (RFFDL) is a data-driven IoT authentication technique that leverages the unique hardware-level manufacturing imperfections associated with a particular device to recognize (fingerprint) the device based on variations introduced in the transmitted waveform. The proposed SignCRF is a scalable, channel-agnostic, data-driven radio authentication platform with unmatched precision in fingerprinting wireless devices based on their unique manufacturing impairments and independent of the dynamic channel irregularities caused by mobility. SignCRF consists of (i) a baseline classifier finely trained to authenticate devices with high accuracy and at scale; (ii) an environment translator carefully designed and trained to remove the dynamic channel impact from RF signals while maintaining the radio's specific signature; (iii) a Max-Rule module that selects the highest precision authentication technique between the baseline classifier and the environment translator per radio. We design, train, and validate the performance of SignCRF for multiple technologies in dynamic environments and at scale (100 LoRa and 20 WiFi devices). We demonstrate that SignCRF significantly improves the RFFDL performance by achieving as high as 5x and 8x improvement in correct authentication of WiFi and LoRa devices when compared to the state-of-the-art, respectively.
Fine-tuning ClimateBert transformer with ClimaText for the disclosure analysis of climate-related financial risks
Authors: Eduardo C. Garrido-Merchán, Cristina González-Barthe, María Coronado Vaca
Date: 2023-03-21 00:00:00
Tasks: text-classification, Text Classification, Transfer Learning

In recent years there has been a growing demand from financial agents, especially from particular and institutional investors, for companies to report on climate-related financial risks. A vast amount of information, in text format, can be expected to be disclosed in the short term by firms in order to identify these types of risks in their financial and non financial reports, particularly in response to the growing regulation that is being passed on the matter. To this end, this paper applies state-of-the-art NLP techniques to achieve the detection of climate change in text corpora. We use transfer learning to fine-tune two transformer models, BERT and ClimateBert -a recently published DistillRoBERTa-based model that has been specifically tailored for climate text classification-. These two algorithms are based on the transformer architecture which enables learning the contextual relationships between words in a text. We carry out the fine-tuning process of both models on the novel Clima-Text database, consisting of data collected from Wikipedia, 10K Files Reports and web-based claims. Our text classification model obtained from the ClimateBert fine-tuning process on ClimaText, outperforms the models created with BERT and the current state-of-the-art transformer in this particular problem. Our study is the first one to implement on the ClimaText database the recently published ClimateBert algorithm. Based on our results, it can be said that ClimateBert fine-tuned on ClimaText is an outstanding tool within the NLP pre-trained transformer models that may and should be used by investors, institutional agents and companies themselves to monitor the disclosure of climate risk in financial reports. In addition, our transfer learning methodology is cheap in computational terms, thus allowing any organization to perform it.
Leveraging Foundation Models for Clinical Text Analysis
Authors: Shaina Raza, Syed Raza Bashir
Date: 2023-03-20 00:00:00

Infectious diseases are a significant public health concern globally, and extracting relevant information from scientific literature can facilitate the development of effective prevention and treatment strategies. However, the large amount of clinical data available presents a challenge for information extraction. To address this challenge, this study proposes a natural language processing (NLP) framework that uses a pre-trained transformer model fine-tuned on task-specific data to extract key information related to infectious diseases from free-text clinical data. The proposed framework includes three components: a data layer for preparing datasets from clinical texts, a foundation model layer for entity extraction, and an assessment layer for performance analysis. The results of the evaluation indicate that the proposed method outperforms standard methods, and leveraging prior knowledge through the pre-trained transformer model makes it useful for investigating other infectious diseases in the future.
Capabilities of GPT-4 on Medical Challenge Problems
Authors: Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, Eric Horvitz
Date: 2023-03-20 00:00:00
Tasks: counterfactual, Memorization, Natural Language Understanding

Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation across various domains, including medicine. We present a comprehensive evaluation of GPT-4, a state-of-the-art LLM, on medical competency examinations and benchmark datasets. GPT-4 is a general-purpose model that is not specialized for medical problems through training or engineered to solve clinical tasks. Our analysis covers two sets of official practice materials for the USMLE, a three-step examination program used to assess clinical competency and grant licensure in the United States. We also evaluate performance on the MultiMedQA suite of benchmark datasets. Beyond measuring model performance, experiments were conducted to investigate the influence of test questions containing both text and images on model performance, probe for memorization of content during training, and study probability calibration, which is of critical importance in high-stakes applications like medicine. Our results show that GPT-4, without any specialized prompt crafting, exceeds the passing score on USMLE by over 20 points and outperforms earlier general-purpose models (GPT-3.5) as well as models specifically fine-tuned on medical knowledge (Med-PaLM, a prompt-tuned version of Flan-PaLM 540B). In addition, GPT-4 is significantly better calibrated than GPT-3.5, demonstrating a much-improved ability to predict the likelihood that its answers are correct. We also explore the behavior of the model qualitatively through a case study that shows the ability of GPT-4 to explain medical reasoning, personalize explanations to students, and interactively craft new counterfactual scenarios around a medical case. Implications of the findings are discussed for potential uses of GPT-4 in medical education, assessment, and clinical practice, with appropriate attention to challenges of accuracy and safety.
PACO: Provocation Involving Action, Culture, and Oppression
Authors: Vaibhav Garg, Ganning Xu, Munindar P. Singh
Date: 2023-03-19 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

In India, people identify with a particular group based on certain attributes such as religion. The same religious groups are often provoked against each other. Previous studies show the role of provocation in increasing tensions between India's two prominent religious groups: Hindus and Muslims. With the advent of the Internet, such provocation also surfaced on social media platforms such as WhatsApp. By leveraging an existing dataset of Indian WhatsApp posts, we identified three categories of provoking sentences against Indian Muslims. Further, we labeled 7,000 sentences for three provocation categories and called this dataset PACO. We leveraged PACO to train a model that can identify provoking sentences from a WhatsApp post. Our best model is fine-tuned RoBERTa and achieved a 0.851 average AUC score over five-fold cross-validation. Automatically identifying provoking sentences could stop provoking text from reaching out to the masses, and can prevent possible discrimination or violence against the target religious group. Further, we studied the provocative speech through a pragmatic lens, by identifying the dialog acts and impoliteness super-strategies used against the religious group.
Requirement Formalisation using Natural Language Processing and Machine Learning: A Systematic Review
Authors: Shekoufeh Kolahdouz-Rahimi, Kevin Lano, Chenghua Lin
Date: 2023-03-18 00:00:00
Tasks: Systematic Literature Review

Improvement of software development methodologies attracts developers to automatic Requirement Formalisation (RF) in the Requirement Engineering (RE) field. The potential advantages by applying Natural Language Processing (NLP) and Machine Learning (ML) in reducing the ambiguity and incompleteness of requirement written in natural languages is reported in different studies. The goal of this paper is to survey and classify existing work on NLP and ML for RF, identifying challenges in this domain and providing promising future research directions. To achieve this, we conducted a systematic literature review to outline the current state-of-the-art of NLP and ML techniques in RF by selecting 257 papers from common used libraries. The search result is filtered by defining inclusion and exclusion criteria and 47 relevant studies between 2012 and 2022 are selected. We found that heuristic NLP approaches are the most common NLP techniques used for automatic RF, primary operating on structured and semi-structured data. This study also revealed that Deep Learning (DL) technique are not widely used, instead classical ML techniques are predominant in the surveyed studies. More importantly, we identified the difficulty of comparing the performance of different approaches due to the lack of standard benchmark cases for RF.
GBO:AMulti-Granularity Optimization Algorithm via Granular-ball for Continuous Problems
Authors: Shuyin Xia, Xinyu Lin, Guan Wang, De-Gang Chen, Sen Zhao, Guoyin Wang, Jing Liang
Date: 2023-03-18 00:00:00
Tasks: Evolutionary Algorithms

Optimization problems aim to find the optimal solution, which is becoming increasingly complex and difficult to solve. Traditional evolutionary optimization methods always overlook the granular characteristics of solution space. In the real scenario of numerous optimizations, the solution space is typically partitioned into sub-regions characterized by varying degree distributions. These sub-regions present different granularity characteristics at search potential and difficulty. Considering the granular characteristics of the solution space, the number of coarse-grained regions is smaller than the number of points, so the calculation is more efficient. On the other hand, coarse-grained characteristics are not easily affected by fine-grained sample points, so the calculation is more robust. To this end, this paper proposes a new multi-granularity evolutionary optimization method, namely the Granular-ball Optimization (GBO) algorithm, which characterizes and searches the solution space from coarse to fine. Specifically, using granular-balls instead of traditional points for optimization increases the diversity and robustness of the random search process. At the same time, the search range in different iteration processes is limited by the radius of granular-balls, covering the solution space from large to small. The mechanism of granular-ball splitting is applied to continuously split and evolve the large granular-balls into smaller ones for refining the solution space. Extensive experiments on commonly used benchmarks have shown that GBO outperforms popular and advanced evolutionary algorithms. The code can be found in the supporting materials.
Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review
Authors: Lixiang Yan, Lele Sha, Linxuan Zhao, Yuheng Li, Roberto Martinez-Maldonado, Guanliang Chen, Xinyu Li, Yueqiao Jin, Dragan Gašević
Date: 2023-03-17 00:00:00
Tasks: Question Generation, Question-Generation

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (e.g., question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency, and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (e.g., GPT-3/4), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models.
Revealing Weaknesses of Vietnamese Language Models Through Unanswerable Questions in Machine Reading Comprehension
Authors: Son Quoc Tran, Phong Nguyen-Thuan Do, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
Date: 2023-03-16 00:00:00
Tasks: Machine Reading Comprehension, Reading Comprehension, Vietnamese Language Models, Vietnamese Machine Reading Comprehension

Although the curse of multilinguality significantly restricts the language abilities of multilingual models in monolingual settings, researchers now still have to rely on multilingual models to develop state-of-the-art systems in Vietnamese Machine Reading Comprehension. This difficulty in researching is because of the limited number of high-quality works in developing Vietnamese language models. In order to encourage more work in this research field, we present a comprehensive analysis of language weaknesses and strengths of current Vietnamese monolingual models using the downstream task of Machine Reading Comprehension. From the analysis results, we suggest new directions for developing Vietnamese language models. Besides this main contribution, we also successfully reveal the existence of artifacts in Vietnamese Machine Reading Comprehension benchmarks and suggest an urgent need for new high-quality benchmarks to track the progress of Vietnamese Machine Reading Comprehension. Moreover, we also introduced a minor but valuable modification to the process of annotating unanswerable questions for Machine Reading Comprehension from previous work. Our proposed modification helps improve the quality of unanswerable questions to a higher level of difficulty for Machine Reading Comprehension systems to solve.
Ablating Concepts in Text-to-Image Diffusion Models
Authors: Nupur Kumari, Bingliang Zhang, Sheng-Yu Wang, Eli Shechtman, Richard Zhang, Jun-Yan Zhu
Date: 2023-03-23 00:00:00
Proceeding: ICCV 2023 1

Large-scale text-to-image diffusion models can generate high-fidelity images with powerful compositional ability. However, these models are typically trained on an enormous amount of Internet data, often containing copyrighted material, licensed images, and personal photos. Furthermore, they have been found to replicate the style of various living artists or memorize exact training samples. How can we remove such copyrighted concepts or images without retraining the model from scratch? To achieve this goal, we propose an efficient method of ablating concepts in the pretrained model, i.e., preventing the generation of a target concept. Our algorithm learns to match the image distribution for a target style, instance, or text prompt we wish to ablate to the distribution corresponding to an anchor concept. This prevents the model from generating target concepts given its text condition. Extensive experiments show that our method can successfully prevent the generation of the ablated concept while preserving closely related concepts in the model.
Persistent Nature: A Generative Model of Unbounded 3D Worlds
Authors: Lucy Chai, Richard Tucker, Zhengqi Li, Phillip Isola, Noah Snavely
Date: 2023-03-23 00:00:00
Proceeding: CVPR 2023 1
Tasks: Decoder, Scene Generation

Despite increasingly realistic image quality, recent 3D image generative models often operate on 3D volumes of fixed extent with limited camera motions. We investigate the task of unconditionally synthesizing unbounded nature scenes, enabling arbitrarily large camera motion while maintaining a persistent 3D world model. Our scene representation consists of an extendable, planar scene layout grid, which can be rendered from arbitrary camera poses via a 3D decoder and volume rendering, and a panoramic skydome. Based on this representation, we learn a generative world model solely from single-view internet photos. Our method enables simulating long flights through 3D landscapes, while maintaining global scene consistency--for instance, returning to the starting point yields the same view of the scene. Our approach enables scene extrapolation beyond the fixed bounds of current 3D generative models, while also supporting a persistent, camera-independent world representation that stands in contrast to auto-regressive 3D prediction models. Our project page: https://chail.github.io/persistent-nature/.
SAOR: Single-View Articulated Object Reconstruction
Authors: Mehmet Aygün, Oisin Mac Aodha
Date: 2023-03-23 00:00:00
Proceeding: CVPR 2024 1
Tasks: Diversity, Object, Object Reconstruction

We introduce SAOR, a novel approach for estimating the 3D shape, texture, and viewpoint of an articulated object from a single image captured in the wild. Unlike prior approaches that rely on pre-defined category-specific 3D templates or tailored 3D skeletons, SAOR learns to articulate shapes from single-view image collections with a skeleton-free part-based model without requiring any 3D object shape priors. To prevent ill-posed solutions, we propose a cross-instance consistency loss that exploits disentangled object shape deformation and articulation. This is helped by a new silhouette-based sampling mechanism to enhance viewpoint diversity during training. Our method only requires estimated object silhouettes and relative depth maps from off-the-shelf pre-trained networks during training. At inference time, given a single-view image, it efficiently outputs an explicit mesh representation. We obtain improved qualitative and quantitative results on challenging quadruped animals compared to relevant existing work.
Position-Guided Point Cloud Panoptic Segmentation Transformer
Authors: Zeqi Xiao, Wenwei Zhang, Tai Wang, Chen Change Loy, Dahua Lin, Jiangmiao Pang
Date: 2023-03-23 00:00:00
Tasks: Instance Segmentation, Panoptic Segmentation, Point Cloud Segmentation, Position, Segmentation, Semantic Segmentation

DEtection TRansformer (DETR) started a trend that uses a group of learnable queries for unified visual perception. This work begins by applying this appealing paradigm to LiDAR-based point cloud segmentation and obtains a simple yet effective baseline. Although the naive adaptation obtains fair results, the instance segmentation performance is noticeably inferior to previous works. By diving into the details, we observe that instances in the sparse point clouds are relatively small to the whole scene and often have similar geometry but lack distinctive appearance for segmentation, which are rare in the image domain. Considering instances in 3D are more featured by their positional information, we emphasize their roles during the modeling and design a robust Mixed-parameterized Positional Embedding (MPE) to guide the segmentation process. It is embedded into backbone features and later guides the mask prediction and query update processes iteratively, leading to Position-Aware Segmentation (PA-Seg) and Masked Focal Attention (MFA). All these designs impel the queries to attend to specific regions and identify various instances. The method, named Position-guided Point cloud Panoptic segmentation transFormer (P3Former), outperforms previous state-of-the-art methods by 3.4% and 1.2% PQ on SemanticKITTI and nuScenes benchmark, respectively. The source code and models are available at https://github.com/SmartBot-PJLab/P3Former .