pwc-archive/papers-with-abstracts huggingface dataset:
WorldCraft: Photo-Realistic 3D World Creation and Customization via LLM Agents
Authors: Xinhang Liu, Chi-Keung Tang, Yu-Wing Tai
Date: 2025-02-21 00:00:00
Tasks: Large Language Model

Constructing photorealistic virtual worlds has applications across various fields, but it often requires the extensive labor of highly trained professionals to operate conventional 3D modeling software. To democratize this process, we introduce WorldCraft, a system where large language model (LLM) agents leverage procedural generation to create indoor and outdoor scenes populated with objects, allowing users to control individual object attributes and the scene layout using intuitive natural language commands. In our framework, a coordinator agent manages the overall process and works with two specialized LLM agents to complete the scene creation: ForgeIt, which integrates an ever-growing manual through auto-verification to enable precise customization of individual objects, and ArrangeIt, which formulates hierarchical optimization problems to achieve a layout that balances ergonomic and aesthetic considerations. Additionally, our pipeline incorporates a trajectory control agent, allowing users to animate the scene and operate the camera through natural language interactions. Our system is also compatible with off-the-shelf deep 3D generators to enrich scene assets. Through evaluations and comparisons with state-of-the-art methods, we demonstrate the versatility of WorldCraft, ranging from single-object customization to intricate, large-scale interior and exterior scene designs. This system empowers non-professionals to bring their creative visions to life.
Almost AI, Almost Human: The Challenge of Detecting AI-Polished Writing
Authors: Shoumik Saha, Soheil Feizi
Date: 2025-02-21 00:00:00
Tasks: Text Generation

The growing use of large language models (LLMs) for text generation has led to widespread concerns about AI-generated content detection. However, an overlooked challenge is AI-polished text, where human-written content undergoes subtle refinements using AI tools. This raises a critical question: should minimally polished text be classified as AI-generated? Such classification can lead to false plagiarism accusations and misleading claims about AI prevalence in online content. In this study, we systematically evaluate twelve state-of-the-art AI-text detectors using our AI-Polished-Text Evaluation (APT-Eval) dataset, which contains 14.7K samples refined at varying AI-involvement levels. Our findings reveal that detectors frequently flag even minimally polished text as AI-generated, struggle to differentiate between degrees of AI involvement, and exhibit biases against older and smaller models. These limitations highlight the urgent need for more nuanced detection methodologies.
Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistance
Authors: Akos Nagy, Yannis Spyridis, Vasileios Argyriou
Date: 2025-02-21 00:00:00
Tasks: Data Integration, Information Retrieval, RAG, Response Generation, Retrieval, Retrieval-augmented Generation

This paper presents a detailed evaluation of a Retrieval-Augmented Generation (RAG) system that integrates large language models (LLMs) to enhance information retrieval and instruction generation for maintenance personnel across diverse data formats. We assessed the performance of eight LLMs, emphasizing key metrics such as response speed and accuracy, which were quantified using BLEU and METEOR scores. Our findings reveal that advanced models like GPT-4 and GPT-4o-mini significantly outperform their counterparts, particularly when addressing complex queries requiring multi-format data integration. The results validate the system's ability to deliver timely and accurate responses, highlighting the potential of RAG frameworks to optimize maintenance operations. Future research will focus on refining retrieval techniques for these models and enhancing response generation, particularly for intricate scenarios, ultimately improving the system's practical applicability in dynamic real-world environments.
M2LADS Demo: A System for Generating Multimodal Learning Analytics Dashboards
Authors: Alvaro Becerra, Roberto Daza, Ruth Cobos, Aythami Morales, Julian Fierrez
Date: 2025-02-21 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

We present a demonstration of a web-based system called M2LADS ("System for Generating Multimodal Learning Analytics Dashboards"), designed to integrate, synchronize, visualize, and analyze multimodal data recorded during computer-based learning sessions with biosensors. This system presents a range of biometric and behavioral data on web-based dashboards, providing detailed insights into various physiological and activity-based metrics. The multimodal data visualized include electroencephalogram (EEG) data for assessing attention and brain activity, heart rate metrics, eye-tracking data to measure visual attention, webcam video recordings, and activity logs of the monitored tasks. M2LADS aims to assist data scientists in two key ways: (1) by providing a comprehensive view of participants' experiences, displaying all data categorized by the activities in which participants are engaged, and (2) by synchronizing all biosignals and videos, facilitating easier data relabeling if any activity information contains errors.
Time Warp: The Gap Between Developers' Ideal vs Actual Workweeks in an AI-Driven Era
Authors: Sukrit Kumar, Drishti Goel, Thomas Zimmermann, Brian Houck, B. Ashok, Chetan Bansal
Date: 2025-02-21 00:00:00

Software developers balance a variety of different tasks in a workweek, yet the allocation of time often differs from what they consider ideal. Identifying and addressing these deviations is crucial for organizations aiming to enhance the productivity and well-being of the developers. In this paper, we present the findings from a survey of 484 software developers at Microsoft, which aims to identify the key differences between how developers would like to allocate their time during an ideal workweek versus their actual workweek. Our analysis reveals significant deviations between a developer's ideal workweek and their actual workweek, with a clear correlation: as the gap between these two workweeks widens, we observe a decline in both productivity and satisfaction. By examining these deviations in specific activities, we assess their direct impact on the developers' satisfaction and productivity. Additionally, given the growing adoption of AI tools in software engineering, both in the industry and academia, we identify specific tasks and areas that could be strong candidates for automation. In this paper, we make three key contributions: 1) We quantify the impact of workweek deviations on developer productivity and satisfaction 2) We identify individual tasks that disproportionately affect satisfaction and productivity 3) We provide actual data-driven insights to guide future AI automation efforts in software engineering, aligning them with the developers' requirements and ideal workflows for maximizing their productivity and satisfaction.
ComposeOn Academy: Transforming Melodic Ideas into Complete Compositions Integrating Music Learning
Authors: Hongxi Pu, Futian Jiang, Zihao Chen, Xingyue Song
Date: 2025-02-21 00:00:00

Music composition has long been recognized as a significant art form. However, existing digital audio workstations and music production software often present high entry barriers for users lacking formal musical training. To address this, we introduce ComposeOn, a music theory-based tool designed for users with limited musical knowledge. ComposeOn enables users to easily extend their melodic ideas into complete compositions and offers simple editing features. By integrating music theory, it explains music creation at beginner, intermediate, and advanced levels. Our user study (N=10) compared ComposeOn with the baseline method, Suno AI, demonstrating that ComposeOn provides a more accessible and enjoyable composing and learning experience for individuals with limited musical skills. ComposeOn bridges the gap between theory and practice, offering an innovative solution as both a composition aid and music education platform. The study also explores the differences between theory-based music creation and generative music, highlighting the former's advantages in personal expression and learning.
Understand User Opinions of Large Language Models via LLM-Powered In-the-Moment User Experience Interviews
Authors: Mengqiao Liu, Tevin Wang, Cassandra A. Cohen, Sarah Li, Chenyan Xiong
Date: 2025-02-21 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Which large language model (LLM) is better? Every evaluation tells a story, but what do users really think about current LLMs? This paper presents CLUE, an LLM-powered interviewer that conducts in-the-moment user experience interviews, right after users interact with LLMs, and automatically gathers insights about user opinions from massive interview logs. We conduct a study with thousands of users to understand user opinions on mainstream LLMs, recruiting users to first chat with a target LLM and then be interviewed by CLUE. Our experiments demonstrate that CLUE captures interesting user opinions, e.g., the bipolar views on the displayed reasoning process of DeepSeek-R1 and demands for information freshness and multi-modality. Our code and data are at https://github.com/cxcscmu/LLM-Interviewer.
BP-GPT: Auditory Neural Decoding Using fMRI-prompted LLM
Authors: Xiaoyu Chen, Changde Du, Che Liu, Yizhe WANG, Huiguang He
Date: 2025-02-21 00:00:00

Decoding language information from brain signals represents a vital research area within brain-computer interfaces, particularly in the context of deciphering the semantic information from the fMRI signal. Although existing work uses LLM to achieve this goal, their method does not use an end-to-end approach and avoids the LLM in the mapping of fMRI-to-text, leaving space for the exploration of the LLM in auditory decoding. In this paper, we introduce a novel method, the Brain Prompt GPT (BP-GPT). By using the brain representation that is extracted from the fMRI as a prompt, our method can utilize GPT-2 to decode fMRI signals into stimulus text. Further, we introduce the text prompt and align the fMRI prompt to it. By introducing the text prompt, our BP-GPT can extract a more robust brain prompt and promote the decoding of pre-trained LLM. We evaluate our BP-GPT on the open-source auditory semantic decoding dataset and achieve a significant improvement up to 4.61 on METEOR and 2.43 on BERTScore across all the subjects compared to the state-of-the-art method. The experimental results demonstrate that using brain representation as a prompt to further drive LLM for auditory neural decoding is feasible and effective. The code is available at https://github.com/1994cxy/BP-GPT.
Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns
Authors: Naiming Liu, Shashank Sonkar, Richard G. Baraniuk
Date: 2025-02-21 00:00:00
Tasks: Distractor Generation, Multiple-choice

Large Language Models (LLMs) have demonstrated remarkable capabilities in various educational tasks, yet their alignment with human learning patterns, particularly in predicting which incorrect options students are most likely to select in multiple-choice questions (MCQs), remains underexplored. Our work investigates the relationship between LLM generation likelihood and student response distributions in MCQs with a specific focus on distractor selections. We collect a comprehensive dataset of MCQs with real-world student response distributions to explore two fundamental research questions: (1). RQ1 - Do the distractors that students more frequently select correspond to those that LLMs assign higher generation likelihood to? (2). RQ2 - When an LLM selects a incorrect choice, does it choose the same distractor that most students pick? Our experiments reveals moderate correlations between LLM-assigned probabilities and student selection patterns for distractors in MCQs. Additionally, when LLMs make mistakes, they are more likley to select the same incorrect answers that commonly mislead students, which is a pattern consistent across both small and large language models. Our work provides empirical evidence that despite LLMs' strong performance on generating educational content, there remains a gap between LLM's underlying reasoning process and human cognitive processes in identifying confusing distractors. Our findings also have significant implications for educational assessment development. The smaller language models could be efficiently utilized for automated distractor generation as they demonstrate similar patterns in identifying confusing answer choices as larger language models. This observed alignment between LLMs and student misconception patterns opens new opportunities for generating high-quality distractors that complement traditional human-designed distractors.
The Imitation Game for Educational AI
Authors: Shashank Sonkar, Naiming Liu, Xinghe Chen, Richard G. Baraniuk
Date: 2025-02-21 00:00:00
Tasks: Distractor Generation, Misconceptions

As artificial intelligence systems become increasingly prevalent in education, a fundamental challenge emerges: how can we verify if an AI truly understands how students think and reason? Traditional evaluation methods like measuring learning gains require lengthy studies confounded by numerous variables. We present a novel evaluation framework based on a two-phase Turing-like test. In Phase 1, students provide open-ended responses to questions, revealing natural misconceptions. In Phase 2, both AI and human experts, conditioned on each student's specific mistakes, generate distractors for new related questions. By analyzing whether students select AI-generated distractors at rates similar to human expert-generated ones, we can validate if the AI models student cognition. We prove this evaluation must be conditioned on individual responses - unconditioned approaches merely target common misconceptions. Through rigorous statistical sampling theory, we establish precise requirements for high-confidence validation. Our research positions conditioned distractor generation as a probe into an AI system's fundamental ability to model student thinking - a capability that enables adapting tutoring, feedback, and assessments to each student's specific needs.
Fostering Inclusion: A Virtual Reality Experience to Raise Awareness of Dyslexia-Related Barriers in University Settings
Authors: José Manuel Alcalde-Llergo, Pilar Aparicio-Martínez, Andrea Zingoni, Sara Pinzi, Enrique Yeguas-Bolívar
Date: 2025-02-20 00:00:00

This work introduces the design, implementation, and validation of a virtual reality (VR) experience aimed at promoting the inclusion of individuals with dyslexia in university settings. Unlike traditional awareness methods, this immersive approach offers a novel way to foster empathy by allowing participants to experience firsthand the challenges faced by students with dyslexia. Specifically, the experience raises awareness by exposing non-dyslexic individuals to the difficulties commonly encountered by dyslexic students. In the virtual environment, participants explore a virtual campus with multiple buildings, navigating between them while completing tasks and simultaneously encountering barriers that simulate some of the challenges faced by individuals with dyslexia. These barriers include reading signs with shifting letters, following directional arrows that may point incorrectly, and dealing with a lack of assistance. The campus is a comprehensive model featuring both indoor and outdoor spaces and supporting various modes of locomotion. To validate the experience, more than 30 non-dyslexic participants from the university environment, mainly professors and students, evaluated it through ad hoc satisfaction surveys. The results indicated heightened awareness of the barriers encountered by students with dyslexia, with participants deeming the experience a valuable tool for increasing visibility and fostering understanding of dyslexic students.
InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback
Authors: Henry Hengyuan Zhao, Wenqi Pei, Yifei Tao, Haiyang Mei, Mike Zheng Shou
Date: 2025-02-20 00:00:00

Existing benchmarks do not test Large Multimodal Models (LMMs) on their interactive intelligence with human users, which is vital for developing general-purpose AI assistants. We design InterFeedback, an interactive framework, which can be applied to any LMM and dataset to assess this ability autonomously. On top of this, we introduce InterFeedback-Bench which evaluates interactive intelligence using two representative datasets, MMMU-Pro and MathVerse, to test 10 different open-source LMMs. Additionally, we present InterFeedback-Human, a newly collected dataset of 120 cases designed for manually testing interactive performance in leading models such as OpenAI-o1 and Claude-3.5-Sonnet. Our evaluation results indicate that even the state-of-the-art LMM, OpenAI-o1, struggles to refine its responses based on human feedback, achieving an average score of less than 50%. Our findings point to the need for methods that can enhance LMMs' capabilities to interpret and benefit from feedback.
A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems
Authors: Lew Lefton, Kexin Rong, Chinar Dankhara, Lila Ghemri, Firdous Kausar, A. Hannibal Hamdallahi
Date: 2025-02-20 00:00:00
Tasks: Natural Language Queries, RAG, Retrieval-augmented Generation

In this paper, we propose a Retrieval Augmented Generation (RAG) agent that maps natural language queries about research topics to precise, machine-interpretable semantic entities. Our approach combines RAG with Socratic dialogue to align a user's intuitive understanding of research topics with established Knowledge Organization Systems (KOSs). The proposed approach will effectively bridge "little semantics" (domain-specific KOS structures) with "big semantics" (broad bibliometric repositories), making complex academic taxonomies more accessible. Such agents have the potential for broad use. We illustrate with a sample application called CollabNext, which is a person-centric knowledge graph connecting people, organizations, and research topics. We further describe how the application design has an intentional focus on HBCUs and emerging researchers to raise visibility of people historically rendered invisible in the current science system.
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding
Authors: Ahmed Heakl, Abdullah Sohail, Mukul Ranjan, Rania Hossam, Ghazi Ahmed, Mohamed El-Geish, Omar Maher, Zhiqiang Shen, Fahad Khan, Salman Khan
Date: 2025-02-20 00:00:00
Tasks: document understanding, Optical Character Recognition, Optical Character Recognition (OCR), RAG, Retrieval-augmented Generation

With the growing adoption of Retrieval-Augmented Generation (RAG) in document processing, robust text recognition has become increasingly critical for knowledge extraction. While OCR (Optical Character Recognition) for English and other languages benefits from large datasets and well-established benchmarks, Arabic OCR faces unique challenges due to its cursive script, right-to-left text flow, and complex typographic and calligraphic features. We present KITAB-Bench, a comprehensive Arabic OCR benchmark that fills the gaps in current evaluation systems. Our benchmark comprises 8,809 samples across 9 major domains and 36 sub-domains, encompassing diverse document types including handwritten text, structured tables, and specialized coverage of 21 chart types for business intelligence. Our findings show that modern vision-language models (such as GPT-4, Gemini, and Qwen) outperform traditional OCR approaches (like EasyOCR, PaddleOCR, and Surya) by an average of 60% in Character Error Rate (CER). Furthermore, we highlight significant limitations of current Arabic OCR models, particularly in PDF-to-Markdown conversion, where the best model Gemini-2.0-Flash achieves only 65% accuracy. This underscores the challenges in accurately recognizing Arabic text, including issues with complex fonts, numeral recognition errors, word elongation, and table structure detection. This work establishes a rigorous evaluation framework that can drive improvements in Arabic document analysis methods and bridge the performance gap with English OCR technologies.
Generalization Guarantees for Representation Learning via Data-Dependent Gaussian Mixture Priors
Authors: Milad Sefidgaran, Abdellatif Zaidi, Piotr Krasnowski
Date: 2025-02-21 00:00:00
Tasks: Representation Learning

We establish in-expectation and tail bounds on the generalization error of representation learning type algorithms. The bounds are in terms of the relative entropy between the distribution of the representations extracted from the training and "test'' datasets and a data-dependent symmetric prior, i.e., the Minimum Description Length (MDL) of the latent variables for the training and test datasets. Our bounds are shown to reflect the "structure" and "simplicity'' of the encoder and significantly improve upon the few existing ones for the studied model. We then use our in-expectation bound to devise a suitable data-dependent regularizer; and we investigate thoroughly the important question of the selection of the prior. We propose a systematic approach to simultaneously learning a data-dependent Gaussian mixture prior and using it as a regularizer. Interestingly, we show that a weighted attention mechanism emerges naturally in this procedure. Our experiments show that our approach outperforms the now popular Variational Information Bottleneck (VIB) method as well as the recent Category-Dependent VIB (CDVIB).
Decoding for Punctured Convolutional and Turbo Codes: A Deep Learning Solution for Protocols Compliance
Authors: Yongli Yan, Linglong Dai
Date: 2025-02-21 00:00:00

Neural network-based decoding methods have shown promise in enhancing error correction performance, but traditional approaches struggle with the challenges posed by punctured codes. In particular, these methods fail to address the complexities of variable code rates and the need for protocol compatibility. This paper presents a unified Long Short-Term Memory (LSTM)-based decoding architecture specifically designed to overcome these challenges. The proposed method unifies punctured convolutional and Turbo codes. A puncture embedding mechanism integrates puncturing patterns directly into the network, enabling seamless adaptation to varying code rates, while balanced bit error rate training ensures robustness across different code lengths, rates, and channels, maintaining protocol flexibility. Extensive simulations in Additive White Gaussian Noise and Rayleigh fading channels demonstrate that the proposed approach outperforms conventional decoding techniques, providing significant improvements in decoding accuracy and robustness. These results underscore the potential of LSTM-based decoding as a promising solution for next-generation artificial intelligence powered communication systems.