pwc-archive/papers-with-abstracts huggingface dataset:
Retrieval-Augmented Generation by Evidence Retroactivity in LLMs
Authors: Liang Xiao, Wen Dai, Shuai Chen, Bin Qin, Chongyang Shi, Haopeng Jing, Tianyu Guo
Date: 2025-01-07 00:00:00
Tasks: Retrieval, Retrieval-augmented Generation

Retrieval-augmented generation has gained significant attention due to its ability to integrate relevant external knowledge, enhancing the accuracy and reliability of the LLMs' responses. Most of the existing methods apply a dynamic multiple retrieval-generating process, to address multi-hop complex questions by decomposing them into sub-problems. However, these methods rely on an unidirectional forward reasoning paradigm, where errors from insufficient reasoning steps or inherent flaws in current retrieval systems are irreversible, potentially derailing the entire reasoning chain. For the first time, this work introduces Retroactive Retrieval-Augmented Generation (RetroRAG), a novel framework to build a retroactive reasoning paradigm. RetroRAG revises and updates the evidence, redirecting the reasoning chain to the correct direction. RetroRAG constructs an evidence-collation-discovery framework to search, generate, and refine credible evidence. It synthesizes inferential evidence related to the key entities in the question from the existing source knowledge and formulates search queries to uncover additional information. As new evidence is found, RetroRAG continually updates and organizes this information, enhancing its ability to locate further necessary evidence. Paired with an Answerer to generate and evaluate outputs, RetroRAG is capable of refining its reasoning process iteratively until a reliable answer is obtained. Empirical evaluations show that RetroRAG significantly outperforms existing methods.
Modality-Invariant Bidirectional Temporal Representation Distillation Network for Missing Multimodal Sentiment Analysis
Authors: Xincheng Wang, Liejun Wang, Yinfeng Yu, Xinxin Jiao
Date: 2025-01-07 00:00:00
Tasks: Multimodal Sentiment Analysis, Representation Learning, Sentiment Analysis

Multimodal Sentiment Analysis (MSA) integrates diverse modalities(text, audio, and video) to comprehensively analyze and understand individuals' emotional states. However, the real-world prevalence of incomplete data poses significant challenges to MSA, mainly due to the randomness of modality missing. Moreover, the heterogeneity issue in multimodal data has yet to be effectively addressed. To tackle these challenges, we introduce the Modality-Invariant Bidirectional Temporal Representation Distillation Network (MITR-DNet) for Missing Multimodal Sentiment Analysis. MITR-DNet employs a distillation approach, wherein a complete modality teacher model guides a missing modality student model, ensuring robustness in the presence of modality missing. Simultaneously, we developed the Modality-Invariant Bidirectional Temporal Representation Learning Module (MIB-TRL) to mitigate heterogeneity.
LatteReview: A Multi-Agent Framework for Systematic Review Automation Using Large Language Models
Authors: Pouria Rouzrokh, Moein Shariatnia
Date: 2025-01-05 00:00:00
Tasks: Decision Making, RAG, Retrieval-augmented Generation

Systematic literature reviews and meta-analyses are essential for synthesizing research insights, but they remain time-intensive and labor-intensive due to the iterative processes of screening, evaluation, and data extraction. This paper introduces and evaluates LatteReview, a Python-based framework that leverages large language models (LLMs) and multi-agent systems to automate key elements of the systematic review process. Designed to streamline workflows while maintaining rigor, LatteReview utilizes modular agents for tasks such as title and abstract screening, relevance scoring, and structured data extraction. These agents operate within orchestrated workflows, supporting sequential and parallel review rounds, dynamic decision-making, and iterative refinement based on user feedback. LatteReview's architecture integrates LLM providers, enabling compatibility with both cloud-based and locally hosted models. The framework supports features such as Retrieval-Augmented Generation (RAG) for incorporating external context, multimodal reviews, Pydantic-based validation for structured inputs and outputs, and asynchronous programming for handling large-scale datasets. The framework is available on the GitHub repository, with detailed documentation and an installable package.
Small Language Models (SLMs) Can Still Pack a Punch: A survey
Authors: Shreyas Subramanian, Vikram Elango, Mecit Gungor
Date: 2025-01-03 00:00:00

As foundation AI models continue to increase in size, an important question arises - is massive scale the only path forward? This survey of about 160 papers presents a family of Small Language Models (SLMs) in the 1 to 8 billion parameter range that demonstrate smaller models can perform as well, or even outperform large models. We explore task agnostic, general purpose SLMs, task-specific SLMs and techniques to create SLMs that can guide the community to build models while balancing performance, efficiency, scalability and cost. Furthermore we define and characterize SLMs' effective sizes, representing increased capability with respect to LLMs.
Data driven discovery of human mobility models
Authors: Hao Guo, Weiyu Zhang, Junjie Yang, Yuanqiao Hou, Lei Dong, Yu Liu
Date: 2025-01-10 00:00:00
Tasks: Symbolic Regression

Human mobility is a fundamental aspect of social behavior, with broad applications in transportation, urban planning, and epidemic modeling. However, for decades new mathematical formulas to model mobility phenomena have been scarce and usually discovered by analogy to physical processes, such as the gravity model and the radiation model. These sporadic discoveries are often thought to rely on intuition and luck in fitting empirical data. Here, we propose a systematic approach that leverages symbolic regression to automatically discover interpretable models from human mobility data. Our approach finds several well-known formulas, such as the distance decay effect and classical gravity models, as well as previously unknown ones, such as an exponential-power-law decay that can be explained by the maximum entropy principle. By relaxing the constraints on the complexity of model expressions, we further show how key variables of human mobility are progressively incorporated into the model, making this framework a powerful tool for revealing the underlying mathematical structures of complex social phenomena directly from observational data.
Quantum Simplicial Neural Networks
Authors: Simone Piperno, Claudio Battiloro, Andrea Ceschini, Francesca Dominici, Paolo Di Lorenzo, Massimo Panella
Date: 2025-01-09 00:00:00
Tasks: Deep Learning

Graph Neural Networks (GNNs) excel at learning from graph-structured data but are limited to modeling pairwise interactions, insufficient for capturing higher-order relationships present in many real-world systems. Topological Deep Learning (TDL) has allowed for systematic modeling of hierarchical higher-order interactions by relying on combinatorial topological spaces such as simplicial complexes. In parallel, Quantum Neural Networks (QNNs) have been introduced to leverage quantum mechanics for enhanced computational and learning power. In this work, we present the first Quantum Topological Deep Learning Model: Quantum Simplicial Networks (QSNs), being QNNs operating on simplicial complexes. QSNs are a stack of Quantum Simplicial Layers, which are inspired by the Ising model to encode higher-order structures into quantum states. Experiments on synthetic classification tasks show that QSNs can outperform classical simplicial TDL models in accuracy and efficiency, demonstrating the potential of combining quantum computing with TDL for processing data on combinatorial topological spaces.
Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks
Authors: David A. R. Robin, Kevin Scaman, Marc Lelarge
Date: 2025-01-10 00:00:00

We present a framework to define a large class of neural networks for which, by construction, training by gradient flow provably reaches arbitrarily low loss when the number of parameters grows. Distinct from the fixed-space global optimality of non-convex optimization, this new form of convergence, and the techniques introduced to prove such convergence, pave the way for a usable deep learning convergence theory in the near future, without overparameterization assumptions relating the number of parameters and training samples. We define these architectures from a simple computation graph and a mechanism to lift it, thus increasing the number of parameters, generalizing the idea of increasing the widths of multi-layer perceptrons. We show that architectures similar to most common deep learning models are present in this class, obtained by sparsifying the weight tensors of usual architectures at initialization. Leveraging tools of algebraic topology and random graph theory, we use the computation graph's geometry to propagate properties guaranteeing convergence to any precision for these large sparse models.
kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search
Authors: Leonardo Delfino, Domenico Erriquez, Silvio Martinico, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini
Date: 2025-01-10 00:00:00
Tasks: Information Retrieval, Quantization, Recommendation Systems

Approximate Nearest Neighbors (ANN) search is a crucial task in several applications like recommender systems and information retrieval. Current state-of-the-art ANN libraries, although being performance-oriented, often lack modularity and ease of use. This translates into them not being fully suitable for easy prototyping and testing of research ideas, an important feature to enable. We address these limitations by introducing kANNolo, a novel research-oriented ANN library written in Rust and explicitly designed to combine usability with performance effectively. kANNolo is the first ANN library that supports dense and sparse vector representations made available on top of different similarity measures, e.g., euclidean distance and inner product. Moreover, it also supports vector quantization techniques, e.g., Product Quantization, on top of the indexing strategies implemented. These functionalities are managed through Rust traits, allowing shared behaviors to be handled abstractly. This abstraction ensures flexibility and facilitates an easy integration of new components. In this work, we detail the architecture of kANNolo and demonstrate that its flexibility does not compromise performance. The experimental analysis shows that kANNolo achieves state-of-the-art performance in terms of speed-accuracy trade-off while allowing fast and easy prototyping, thus making kANNolo a valuable tool for advancing ANN research. Source code available on GitHub: https://github.com/TusKANNy/kannolo.
Recommender Systems for Social Good: The Role of Accountability and Sustainability
Authors: Alan Said
Date: 2025-01-10 00:00:00
Tasks: Fairness, Recommendation Systems

This work examines the role of recommender systems in promoting sustainability, social responsibility, and accountability, with a focus on alignment with the United Nations Sustainable Development Goals (SDGs). As recommender systems become increasingly integrated into daily interactions, they must go beyond personalization to support responsible consumption, reduce environmental impact, and foster social good. We explore strategies to mitigate the carbon footprint of recommendation models, ensure fairness, and implement accountability mechanisms. By adopting these approaches, recommender systems can contribute to sustainable and socially beneficial outcomes, aligning technological advancements with the SDGs focused on environmental sustainability and social well-being.
Social web and Wikipedia: an opportunity to rethink the links between sources' credibility, trust and authority
Authors: Gilles Sahut, André Tricot
Date: 2025-01-10 00:00:00

The Web and its main tools (Google, Wikipedia, Facebook, Twitter) deeply raise and renew fundamental questions, that everyone asks almost every day: Is this information or content true? Can I trust this author or source? These questions are not new, they have been the same with books, newspapers, broadcasting and television, and, more fundamentally, in every human interpersonal communication. This paper is focused on two scientific problems on this issue. The first one is theoretical: to address this issue, many concepts have been used in library and information sciences, communication and psychology. The links between these concepts are not clear: sometimes two concepts are considered as synonymous, sometimes as very different. The second one is historical: sources like Wikipedia deeply challenge the epistemic evaluation of information sources, compared to previous modes of information production. This paper proposes an integrated and simple model considering the relation between a user, a document and an author as human communication. It reduces the problem to three concepts: credibility as a characteristic granted to information depending on its truth-value; trust as the ability to produce credible information; authority when the power to influence of an author is accepted, i.e., when readers accept that the source can modify their opinion, knowledge and decisions. The model describes also two kinds of relationships between the three concepts: an upward link and a downward link. The model is confronted with findings of empirical research on Wikipedia in particular.
LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models
Authors: Hang Yang, Hao Chen, Hui Guo, Yineng Chen, Ching-Sheng Lin, Shu Hu, Jinrong Hu, Xi Wu, Xin Wang
Date: 2024-12-31 00:00:00
Tasks: Medical Question Answering, MedQA, Question Answering, Zero-Shot Learning

Accurate and efficient question-answering systems are essential for delivering high-quality patient care in the medical field. While Large Language Models (LLMs) have made remarkable strides across various domains, they continue to face significant challenges in medical question answering, particularly in understanding domain-specific terminologies and performing complex reasoning. These limitations undermine their effectiveness in critical medical applications. To address these issues, we propose a novel approach incorporating similar case generation within a multi-agent medical question-answering (MedQA) system. Specifically, we leverage the Llama3.1:70B model, a state-of-the-art LLM, in a multi-agent architecture to enhance performance on the MedQA dataset using zero-shot learning. Our method capitalizes on the model's inherent medical knowledge and reasoning capabilities, eliminating the need for additional training data. Experimental results show substantial performance gains over existing benchmark models, with improvements of 7% in both accuracy and F1-score across various medical QA tasks. Furthermore, we examine the model's interpretability and reliability in addressing complex medical queries. This research not only offers a robust solution for medical question answering but also establishes a foundation for broader applications of LLMs in the medical domain.
Multi-Target Detection Based on Camera and Radar Feature Fusion Networks
Authors: CHANG Liang1, 2 ， BAI Jie2 ， HUANG Libo2
Date: 2022-03-01 00:00:00
Proceeding: 北 京 理 工 大 学 学 报 2022 3
Tasks: TAR

A network model for radar and camera feature fusion was proposed to deal with the impact of low
light, rain and fog and other harsh scenes on the detection capability of intelligent driving vision systems. A radar
attention mechanism feature module was constructed based on millimeter wave radar information and attention
model to provide a priori information and increase the weight of the algorithm in the target candidate region for
the feature fusion network. The test results show that, introducing the radar attention mechanism module, the target detection performance of the feature fusion network is significantly better than the detection performance of
that relying on computer vision alone, and the target detection is more robust in complex scenes.
Low-Resource Text-to-Speech Synthesis Using Noise-Augmented Training of ForwardTacotron
Authors: Kishor Kayyar Lakshminarayana, Frank Zalkow, Christian Dittmar, Nicola Pia, Emanuel A. P. Habets
Date: 2025-01-10 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

In recent years, several text-to-speech systems have been proposed to synthesize natural speech in zero-shot, few-shot, and low-resource scenarios. However, these methods typically require training with data from many different speakers. The speech quality across the speaker set typically is diverse and imposes an upper limit on the quality achievable for the low-resource speaker. In the current work, we achieve high-quality speech synthesis using as little as five minutes of speech from the desired speaker by augmenting the low-resource speaker data with noise and employing multiple sampling techniques during training. Our method requires only four high-quality, high-resource speakers, which are easy to obtain and use in practice. Our low-complexity method achieves improved speaker similarity compared to the state-of-the-art zero-shot method HierSpeech++ and the recent low-resource method AdapterMix while maintaining comparable naturalness. Our proposed approach can also reduce the data requirements for speech synthesis for new speakers and languages.
Russian Financial Statements Database: A firm-level collection of the universe of financial statements
Authors: Sergey Bondarkov, Victor Ledenev, Dmitriy Skougarevskiy
Date: 2025-01-10 00:00:00
Tasks: Imputation

The Russian Financial Statements Database (RFSD) is an open, harmonized collection of annual unconsolidated financial statements of the universe of Russian firms in 2011-2023. It is the first open data set with information on every active firm in the country, including non-filing firms. With 56.6 million geolocated firm-year observations gathered from two official sources, the RFSD features multiple end-user quality-of-life improvements such as data imputation, statement articulation, harmonization across data providers and formats, and data enrichment. Extensive internal and external validation shows that most statements articulate well while their aggregates display higher correlation with the regional GDP than the previous gridded GDP data products. We also examine the direction and magnitude of the reporting bias by comparing the universe of firms that are required to file with the actual filers. The RFSD can be used in various economic applications as diverse as calibration of micro-founded models, estimation of markups and productivity, or assessing industry organization and market power.
Deep CNN with late fusion for realtime multimodal emotion recognition
Authors: Chhavi Dixita, Shashank Mouli Satapathy
Date: 2024-04-15 00:00:00
Proceeding: Expert Systems with Applications 2024 4
Tasks: Emotion Recognition, Multimodal Emotion Recognition

Emotion recognition is a fundamental aspect of human communication and plays a crucial role in various
 domains. This project aims at developing an efficient model for real-time multimodal emotion recognition in
 videos of human oration (opinion videos), where the speakers express their opinions about various topics. Four
 separate datasets contributing 20,000 samples for text, 1,440 for audio, 35,889 for images, and 3,879 videos
 for multimodal analysis respectively are used. One model is trained for each of the modalities: fastText for text
 analysis because of its efficiency, robustness to noise, and pre-trained embeddings; customized 1-D CNN for
 audio analysis using its translation invariance, hierarchical feature extraction, scalability, and generalization;
 custom 2-D CNN for image analysis because of its ability to capture local features and handle variations in
 image content. They are tested and combined on the CMU-MOSEI dataset using both bagging and stacking
 to find the most effective architecture. They are then used for real-time analysis of speeches. Each of the
 models is trained on 80% of the datasets, the remaining 20% is used for testing individual and combined
 accuracies in CMU-MOSEI. The emotions finally predicted by the architecture correspond to the six classes
 in the CMU-MOSEI dataset. This cross-dataset training and testing of the models makes them robust and
 efficient for general use, removes reliance on a specific domain or dataset, and adds more data points for
 model training. The proposed architecture was able to achieve an accuracy of 85.85% and an F1-score of 83
 on the CMU-MOSEI dataset.
Automatic code generation from low fidelity graphical user interface sketches using deep learning
Authors: Biniam Behailu Adefris; Ayalew Belay Habtie; Yesuneh Getachew Taye
Date: 2022-11-28 00:00:00
Proceeding: 2022 International Conference on Information and Communication Technology for Development for Africa (ICT4DA) 2022 11
Tasks: Code Generation

In software development, the user interface(UI) design process involves the part from an imagination that begins on a piece of paper where designers share thoughts to the final prototype that is ready to implement. UI designers use a technique of paper prototyping in low-fidelity sketches to implement the conceptual design. Once a design is drawn, it is usually captured within a photograph, then converted to a high-level mockup, and manually translated into a working client-side code by developers. The process consumes considerable effort and also delays the design process. Recently, researchers attempted to recreate UI from their sketches using code generation techniques. In this paper, we present a system for automatic code generation that combines recent advances in computer vision and deep learning for translating a sketched wireframe into a UI skeleton code. The proposed system has two main components: component recognition and code generation. The component recognition learns to extract visual features and their spatial layouts from sketched UI images. The code generator creates the equivalent UI skeleton codes based on the information from the detection model. A layout algorithm has been developed to resolve the overlapping of components and structure the layout. The evaluation result of the component recognition model indicates that the Faster R-CNN with Inception ResNet v2 backbone model achieved a mean average precision (mAP) of 95.5 %, among the other trained models. To evaluate the generator, we conducted a user study based on visual similarities between input image and the rendered UI.