GenAI4ELab/papercli-papers huggingface dataset:
EvalAssist: LLM-as-a-Judge Simplified
Authors: Michael Desmond; Zahra Ashktorab; Werner Geyer; Elizabeth M. Daly; Martín Santillán Cooper; Qian Pan; Rahul Nair; Nico Wagner; Tejaswini Pedapati
Venue: AAAI 2025 (main)

Abstract: We present EvalAssist, a framework that simplifies the LLM- as-a-judge workflow. The system provides an online criteria development environment, where users can interactively build, test, and share custom evaluation criteria in a structured and portable format. A library of LLM based evaluators is made available that incorporates various algorithmic innovations such as token-probability based judgement, positional bias checking, and certainty estimation that help to engender trust in the evaluation process. We have computed extensive benchmarks and also deployed the system internally in our organization with several hundreds of users.
MATWA: A Web Toolkit for Matching Under Preferences
Authors: Frederik Glitzner; David Manlove
Venue: AAAI 2025 (main)

Abstract: Matching markets, in which agents are assigned to one another based on preferences and capacity constraints, are pervasive in various domains. This paper introduces MATWA (https://matwa.optimalmatching.com), a web application that offers the most comprehensive collection to date of algorithms for fundamental matching under preference problem classes. MATWA provides results of algorithm executions and visualisations of structural properties. It is intended to be a resource for the community of researchers, educators and practitioners, supporting experimentation, as well as aiding the understanding of matching algorithms.
An LLM-Guided Tutoring System for Social Skills Training
Authors: Michael Guevarra; Indronil Bhattacharjee; Srijita Das; Christabel Wayllace; Carrie Demmans Epp; Matthew E. Taylor; Alan Tay
Venue: AAAI 2025 (main)

Abstract: Social skills training targets behaviors necessary for success in social interactions. However, traditional classroom training for such skills is often insufficient to teach effective communication — one-to-one interaction in real-world scenarios is preferred to lecture-style information delivery. This paper introduces a framework that allows instructors to collaborate with large language models to dynamically design realistic scenarios for students to communicate. Our framework uses these scenarios to enable student rehearsal, provide immediate feedback and visualize performance for both students and instructors. Unlike traditional intelligent tutoring systems, instructors can easily co-create scenarios with a large language model without technical skills. Additionally, the system generates new scenario branches in real time when existing options don't fit the student's response.
MAFT: Multimodal Automated Fact-Checking via Textualization
Authors: Kazuya Kakizaki; Yuto Matsunaga; Ryo Furukawa
Venue: AAAI 2025 (main)

Abstract: This paper proposes MAFT, a novel multimodal automated fact-checking system capable of handling content in any combination of text, images, videos, and audio. The core idea behind our system is the textualization of multimodal content using various machine learning techniques. MAFT comprehensively analyzes this textualized content along with external information collected via web APIs by large language models (LLMs). MAFT generates interpretable fact-checking reports that include not only verification results but also a detailed verification process. With its adaptability and ability to automatically verify multimodal content, MAFT contributes to the fight against the spread of multimodal misinformation.
SPASCA: Social Presence and Support with Conversational Agent for Persons Living with Dementia
Authors: Ali Köksal; Jingjing Gu; Kotaro Hara; Jing Jiang; Joo-Hwee Lim; Qianli Xu
Venue: AAAI 2025 (main)

Abstract: We present SPASCA - a conversational AI system that promotes psychological and cognitive well-being of persons living with dementia (PLWD). This system features an AI agent that provides social presence and support to PLWD through verbal communications, without physical presence of human caregivers. The system integrates (1) a novel dialogue model that generates dialogue items relevant to the user's experiences and lifestyle, (2) a digital avatar in the form of a talking head with the identity of a caregiver who is familiar to the demented user. We develop prototypes that adopt various interaction modalities and conversational styles and report the pros and cons of different system configurations through expert review. Our system shows the potential of conversational AI for personalized and affordable healthcare services.
Rewind and Render: Towards Factually Accurate Text-to-Video Generation with Distilled Knowledge Retrieval
Authors: Daniel Lee; Arjun Chandra; Yang Zhou; Yunyao Li; Simone Conia
Venue: AAAI 2025 (main)

Abstract: Text-to-Video (T2V) models, despite recent advancements, struggle with factual accuracy, especially for knowledge-dense content. We introduce FACT-V (Factual Accuracy in Content Translation to Video), a system integrating multi-source knowledge retrieval into T2V pipelines. FACT-V offers two key benefits: i) improved factual accuracy of generated videos through dynamically retrieved information, and ii) increased interpretability by providing users with the augmented prompt information. A preliminary evaluation demonstrates the potential of knowledge-augmented approaches in improving the accuracy and reliability of T2V systems, particularly for entity-specific or time-sensitive prompts.
LLM Attributor: Interactive Visual Attribution for LLM Generation
Authors: Seongmin Lee; Zijie J. Wang; Aishwarya Chakravarthy; Alec Helbling; ShengYun Peng; Mansi Phute; Duen Horng (Polo) Chau; Minsuk Kahng
Venue: AAAI 2025 (main)

Abstract: While large language models (LLMs) have shown remarkable capability to generate convincing text across diverse domains, concerns around its potential risks have highlighted the importance of understanding the rationale behind text generation. We present LLM ATTRIBUTOR, a Python library that provides interactive visualizations for training data attribution of an LLM’s text generation. Our library offers a new way to quickly attribute an LLM’s text generation to training data points to inspect model behaviors, enhance its trustworthiness, and compare model-generated text with user-provided text. Thanks to LLM ATTRIBUTOR’s broad support for computational notebooks, users can easily integrate it into their workflow to interactively visualize attributions of their models.
An Automated Explainable Educational Assessment System Built on LLMs
Authors: Jiazheng Li; Artem Bobrov; David West; Cesare Aloisi; Yulan He
Venue: AAAI 2025 (main)

Abstract: In this demo, we present AERA Chat, an automated and explainable educational assessment system designed for interactive and visual evaluations of student responses. This system leverages large language models (LLMs) to generate automated marking and rationale explanations, addressing the challenge of limited explainability in automated educational assessment and the high costs associated with annotation. Our system allows users to input questions and student answers, providing educators and researchers with insights into assessment accuracy and the quality of LLM-assessed rationales. Additionally, it offers advanced visualization and robust evaluation tools, enhancing the usability for educational assessment and facilitating efficient rationale verification.
Pic2Prep: A Multimodal Conversational Agent for Cooking Assistance
Authors: Renjith Prasad Kaippilly Mana; Chathurang Shyalika; Revathy Venkataramanan; Darssan L. Eswaramoorthi; Amit P. Sheth
Venue: AAAI 2025 (main)

Abstract: As the demand for healthier, personalized culinary experiences grows, so does the need for advanced food computation models that offer more than basic nutritional insights. However, current food computation models lack the depth to provide actionable insights like ingredient substitution or alternative cooking actions to suit users’ dietary goals. To address this, we introduce and demonstrate Pic2Prep, a multimodal conversational system that generates detailed cooking instructions, actions and ingredient lists from both images and text provided by users. The system is developed using a novel dataset generated through Stable Diffusion, where the input consists of recipe titles and ingredient lists from the Recipe1M dataset to create synthesized food images with variations. This dataset is used to fine-tune the Bootstrapping Language-Image Pre-training (BLIP) model to extract cooking instructions and ingredients from food images. Pic2Prep also employs the CookGen model, a small-scale custom generative model to derive specific cooking actions from cooking
instructions. A custom mapper, trained on the Mistral model, links these actions to the corresponding ingredients, creating a comprehensive understanding of the cooking process. The
system features an interactive user interface that allows users to input images and ask targeted questions, receiving real-time responses.
Question-guided Insights Generation for Automated Exploratory Data Analysis
Authors: Abhijit Manatkar; Ashlesha Akella; Krishnasuri Narayanam; Sameep Mehta
Venue: AAAI 2025 (main)

Abstract: Exploratory Data Analysis (EDA) derives meaningful insights from extensive and complex datasets. This process typically involves a series of analytical operations to identify the patterns within the data. However, the effectiveness of EDA is often limited by the user's domain knowledge and proficiency in data exploration methods. To overcome these challenges, we developed QUIS, a fully automated EDA system that uncovers insights by generating data-related questions and exploring subspaces in the dataset without prior training. QUIS allows users to control key system parameters such as beam width, beam depth, and expansion factor for subspace selection, the interestingness score for filtering valuable insights, and parameters for managing the quality and quantity of generated questions.
Bidirectional Human-AI Learning in Real-Time Disoriented Balancing
Authors: Sheikh Mannan; Nikhil Krishnaswamy
Venue: AAAI 2025 (main)

Abstract: We present a real-time system that enables bidirectional human-AI learning and teaching in a balancing task that is a realistic analogue of disorientation during piloting and spaceflight. A human subject and autonomous AI model of choice guide each other in maintaining balance using a visual inverted pendulum (VIP) display. We show how AI assistance changes human performance and vice versa.
QGen Studio: An Adaptive Question-Answer Generation, Training and Evaluation Platform
Authors: Movina Moses; Mohab Elkaref; James Barry; Shinnosuke Tanaka; Vishnudev Kuruvanthodi; Nathan Herr; Campbell D Watson; Geeth De Mel
Venue: AAAI 2025 (main)

Abstract: We present QGen Studio: an adaptive question-answer generation, training, and evaluation platform. QGen Studio enables users to leverage large language models (LLMs) to create custom question-answer datasets and fine-tune models on this synthetic data. It features a dataset viewer and model explorer to streamline this process. The dataset viewer provides key metrics and visualizes the context from which the QA pairs are generated, offering insights into data quality. The model explorer supports model comparison, allowing users to contrast the performance of their trained LLMs against other models, supporting performance benchmarking and refinement. QGen Studio delivers an interactive, end-to-end solution for generating QA datasets and training scalable, domain-adaptable models. The studio will be open-sourced soon, allowing users to deploy it locally.
ECLAIR: Enhanced Clarification for Interactive Responses in an Enterprise AI Assistant
Authors: John Murzaku; Zifan Liu; Vaishnavi Muppala; Md Mehrab Tanjim; Xiang Chen; Yunyao Li
Venue: AAAI 2025 (main)

Abstract: Large language models (LLMs) have shown remarkable progress in understanding and generating natural language across various applications. However, they often struggle with resolving ambiguities in real-world, enterprise-level interactions, where context and domain-specific knowledge play a crucial role. In this demonstration, we introduce ECLAIR (Enhanced CLArification for Interactive Responses), a multi-agent framework for interactive disambiguation. ECLAIR enhances ambiguous user query clarification through an interactive process where custom agents are defined, ambiguity reasoning is conducted by the agents, clarification questions are generated, and user feedback is leveraged to refine the final response. When tested on real-world customer data, ECLAIR demonstrates significant improvements in clarification question generation compared to standard few-shot methods.
Speech Is Not Enough: Interpreting Nonverbal Indicators of Common Knowledge and Engagement
Authors: Derek Palmer; Yifan Zhu; Kenneth Lai; Hannah VanderHoeven; Mariah Bradford; Ibrahim Khebour; Carlos Mabrey; Jack Fitzgerald; Nikhil Krishnaswamy; Martha Palmer; James Pustejovsky
Venue: AAAI 2025 (main)

Abstract: Our goal is to develop an AI Partner that can provide support for group problem solving and social dynamics. In multi-party working group environments, multimodal analytics is crucial for identifying non-verbal interactions of group members. In conjunction with their verbal participation, this creates an holistic understanding of collaboration and engagement that provides necessary context for the AI Partner. In this demo, we illustrate our present capabilities at detecting and tracking nonverbal behavior in student task-oriented interactions in the classroom, and the implications for tracking common ground and engagement.
SDAS: Semantic Data Acquisition System for Minimizing Redundancy and Maximizing Diversity
Authors: Yeseung Park; Hyunse Yoon; Jungwoo Huh; Jungsu Kim; Jeongwook Choi; Sanghoon Lee
Venue: AAAI 2025 (main)

Abstract: In this paper, we propose SDAS, a new motion data assessment and storage system designed to acquire new motion data with reduced redundancy and maximizing diversity. SDAS collects data in the field, retrieves the most similar data from the database in real-time, and provides visualization tools that allow for the comparison of differences between the capture data and the stored data. Through this system, researchers can efficiently build and manage a database. The demonstration video is available at https://youtu.be/vqW0uMDnZTw.
MicroFiberDetect: An Application for the Detection of Microfibres in Wastewater Sludge Based on CNNs
Authors: Félix Martí Pérez; Ana Domínguez-Rodríguez; Cèsar Ferri; Carlos Monserrat
Venue: AAAI 2025 (main)

Abstract: Microplastics and microfibres are now widespread in aquatic ecosystems, as oceans and rivers. A serious portion of these microplastics come from urban wastewater treatment plants. Traditional methods for detecting and quantifying them are labour-intensive and time-consuming. This paper introduces MicroFiberDetect, a novel application designed to enhance the detection and quantification of microfibres within sludge samples. Leveraging the power of deep learning, this innovative tool provides detection accuracy and insights into the size and colour of each identified fibre. Reducing time and manpower required for analysis while increasing accuracy and throughput. The application has been deployed as a desktop application that allows field experts to quantify and analyse microfibres in sludge samples.
GODDS: The Global Online Deepfake Detection System
Authors: Marco Postiglione; Julian Baldwin; Natalia Denisenko; Luke Fosdick; Chongyang Gao; Isabel Gortner; Chiara Pulice; Sarit Kraus; V.S. Subrahmanian
Venue: AAAI 2025 (main)

Abstract: Fake audios, videos, and images are now proliferating widely. We developed GODDS, the Global Online Deepfake Detection system, for a specific user community, namely journalists. GODDS leverages an ensemble of deepfake detectors, along with a human in the loop, to provide a deepfake report on each submitted video/image/audio or VIA artifact submitted to the system. To date, VIA artifacts submitted by over 50 journalists from outlets such as the New York Times, Wall Street Journal, CNN, Agence France Press, and others have been run through GODDS. Unlike other deepfake detection systems, GODDS doesn't just focus on the submitted artifact but automatically derives context about the subject of the VIA artifact. Because context is not always available on all subjects, GODDS focuses on alleged deepfakes of high profile individuals, organizations, and events, where there is likely to be considerable contextual information.
Accessible Hardware Implementation for Multi-Agent Collective Construction
Authors: Martin Rameš; Pavel Surynek
Venue: AAAI 2025 (main)

Abstract: We propose a 2D simulation system for multi-agent collective construction (MACC) based on simple line-following intelligent machines (SLIM) - small differential drive mobile robots. Our MACC-SLIM system alleviates the high upfront cost of implementing MACC on real hardware. Our system builds upon widely available resources, namely a standard LCD screen and commodity mobile robots, allowing researchers and schools easier access to MACC hardware implementation. We test the system on plans generated by an optimal state-of-the-art MACC algorithm, demonstrating there are still non-insignificant synchronization delays. The MACC-SLIM system allows us to observe bottlenecks, parallelism, and possible execution failures of plans generated by the MACC algorithms.
StarVector: Generating Scalable Vector Graphics Code from Images and Text
Authors: Juan A. Rodriguez; Abhay Puri; Shubham Agarwal; Issam H. Laradji; Sai Rajeswar; David Vazquez; Christopher Pal; Marco Pedersoli
Venue: AAAI 2025 (main)

Abstract: Scalable Vector Graphics (SVG) have become integral to modern image rendering applications due to their infinite scalability and versatility, especially in graphic design and web development. SVGs are essentially long strings of code that adhere to a structured syntax with validity constraints. With the rise of large language models, which excel at generating code in various languages, we aim to generate SVG code in a similar way. Our findings show that a vision-language model can be conditioned to produce valid SVG code that closely resembles input images, effectively enabling vectorization. Additionally, we harness the rich SVG syntax, encompassing all possible primitives—such as lines, paths, polygons, text, and effects like color gradients—that previous methods often missed. We briefly explain how the StarVector model operates, primarily leveraging a vision-language transformer architecture to generate SVG code. We also detail our training and inference procedures. Finally, we provide an interactive demo that allows users to input an image and generate its SVG code autoregressively, featuring real-time rendering that visually demonstrates the SVG generation process.
Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters
Authors: Soumyendu Sarkar; Avisek Naug; Antonio Guillen; Vineet Gundecha; Ricardo Luna Gutiérrez; Sahand Ghorbanpour; Sajad Mousavi; Ashwin Ramesh Babu; Desik Rengarajan; Cullen Bash
Venue: AAAI 2025 (main)

Abstract: Reducing the environmental impact of cloud computing requires efficient workload distribution across geographically dispersed Data Center Clusters (DCCs) and simultaneously optimizing liquid and air (HVAC) cooling with time shift of workloads within individual data centers (DC). This paper introduces Green-DCC, which proposes a Reinforcement Learning (RL) based hierarchical controller to optimize both workload and liquid cooling dynamically in a DCC. By incorporating factors like weather, carbon intensity, and resource availability, Green-DCC addresses realistic constraints and interdependencies. We demonstrate how the system optimizes multiple data centers synchronously, enabling the scope of digital twins, and compare the performance of various RL approaches based on carbon emissions and sustainability metrics while also offering a framework and benchmark simulation for broader ML research in sustainability.
SuBiTO: Synopsis-based Training Optimization for Continuous Real-Time Neural Learning over Big Streaming Data
Authors: Errikos Streviniotis; George Klioumis; Nikos Giatrakos
Venue: AAAI 2025 (main)

Abstract: In machine learning applications over Big streaming Data, Neural Networks (NNs) are continuously and rapidly trained over voluminous data arriving at high speeds. As soon as a new version of the NN becomes available, it gets deployed for prediction purposes (e.g. classification). The real-time character of such applications greatly depends on the volume and velocity of the data streams, as well as the NN complexity. Training on large volume of ingested streams or using complex NNs, potentially increases accuracy, but may compromise the real-time character of those applications. In this work, we present SuBiTO, a framework that automatically and continuously learns the training time vs accuracy trade-offs as new data stream in and fine tunes: (i) the number, size and type of NN layers; (ii) the size of the ingested data via stream synopses specific parameters; and (iii) the number of training epochs. Finally, SuBiTO suggests optimal sets of such parameters and detects concept drifts, enabling the human operator adapt these parameters on-the-fly, at runtime.
MerryQuery: A Trustworthy LLM-Powered Tool Providing Personalized Support for Educators and Students
Authors: Benyamin Tabarsi; Aditya Basarkar; Xukun Liu; Dongkuan (DK) Xu; Tiffany Barnes
Venue: AAAI 2025 (main)

Abstract: The potential of Large Language Models (LLMs) in education is not trivial, but concerns about academic misconduct, misinformation, and overreliance limit their adoption. To address these issues, we introduce MerryQuery, an AI-powered educational assistant using Retrieval-Augmented Generation (RAG), to provide contextually relevant, course-specific responses. MerryQuery features guided dialogues and source citation to ensure trust and improve student learning. Additionally, it enables instructors to monitor student interactions, customize response granularity, and input multimodal materials without compromising data fidelity. By meeting both student and instructor needs, MerryQuery offers a responsible way to integrate LLMs into educational settings.
Agentic AI for Digital Twin
Authors: Alexander Timms; Abigail Langbridge; Antonis Antonopoulos; Antonis Mygiakis; Eleni Voulgari; Fearghal O'Donncha
Venue: AAAI 2025 (main)

Abstract: The complexity of the shipping industry, dynamic operational drivers, and diverse data sources present significant scalability challenges for digital twins. Agentic Large Language Models (LLMs) augmented with external tools offer a promising solution to accelerate digital twin adoption. Using pre-trained knowledge and reasoning capabilities, these LLMs autonomously select optimal tools and data streams for user-specific queries, enabling language to serve as a universal interface between digital twins and various stakeholders, from technicians to fleet managers. This interface facilitates real-time decision making and insight generation across multiple operational workflows. In this demonstration, we present an interactive agentic digital twin designed to enhance scalability, flexibility, and efficiency in managing the extensive and intricate decision-making requirements of the shipping industry. We showcase the transformative potential of agentic LLMs in reducing complexity and improving the practical application of digital twins, ultimately enabling more efficient operations in real-world settings.