GenAI4ELab/papercli-papers huggingface dataset:
Leveraging Large Language Models for Wind Energy Assessment
Authors: Janice Anta Zebaze; Azanzi Jiomekong; Innocent Souopgui; Germaine Djuidje Kenmoé
Venue: AAAI 2025 (main)

Abstract: Economic growth and development require a consistent supply of energy. Energy which has mainly been supplied from fossil fuels. The impacts of these on the environment such as global warming, raised an alarm on their use. As a result other sources of energy such as wind energy are used as alternatives for electricity production. Wind energy assessment nevertheless faces barriers due to its stochastic nature. This later creates various regimes, which traditional models can't always fit thereby producing poor estimates. In this work, we aim to use Large Language Models (LLMs) to predict the wind potential in a given location. Through this approach, we aim at lifting the barrier on energy problems in developing countries by providing knowledge on the state of wind energy in given locations.
Statistical Methodologies for Decision-Making and Uncertainty Reduction in Machine Learning
Authors: Haofeng Zhang
Venue: AAAI 2025 (main)

Abstract: While advances in machine learning and the expansion of massive datasets have significantly improved predictive accuracy, the translation of these predictions into actionable decisions—alongside a robust understanding of associated risks—remains underexplored. My research focuses on developing methodology and theory in data-driven decision-making and uncertainty quantification that effectively address core data challenges. This paper presents two connected pillars of my research: data-driven contextual optimization, uncertainty quantification and reduction.
Advancing Medical Multimodal Learning and Data Generation with Diffusion Model and LLM
Authors: Yuan Zhong
Venue: AAAI 2025 (main)

Abstract: Synthesizing electronic health records (EHR) is essential for addressing data scarcity, bias, and fairness in healthcare models. EHR data are inherently multimodal and sequential, encompassing structured codes, clinical notes, medical
images, and irregular time intervals. Traditional generative models like GANs and VAEs struggle to capture these complexities, while diffusion-based models offer improvements but remain limited to task-specific applications. To address these challenges, two diffusion-based models, MedDiffusion and EHRPD, have been developed. MedDiffusion enhances health risk prediction by generating synthetic patient data
and capturing visit-level relationships, while EHRPD generates sequential, multimodal EHR data, incorporating temporal interval estimation to improve diversity and fidelity. Future work aims to overcome limitations in multimodal data generation by developing a generalized model capable of handling diverse modalities simultaneously, expanding the applicability of EHR data generation across healthcare tasks.
Using Next Sentence Prediction to Test ChatGPT’s Text Comprehension (Student Abstract)
Authors: Ojas M Agarwal; Madelein Villegas; Jack Mostow
Venue: AAAI 2025 (main)

Abstract: We propose the Next Sentence Prediction (NSP) task as a simple, objective, scalable, automated way to test ChatGPT’s text comprehension. Given a context excerpted from a children’s story, the task is to distinguish the next story sentence from a later sentence in the story. We analyze how ChatGPT’s performance on this task is related to various features of the text, using data from English and Swahili children’s stories.
Hypernetwork Approach to Bayesian MAML (Student Abstract)
Authors: Piotr Borycki; Piotr Kubacki; Marcin Przewięźlikowski; Tomasz Kuśmierczyk; Jacek Tabor; Przemysław Spurek
Venue: AAAI 2025 (main)

Abstract: The main goal of Few-Shot learning algorithms is to enable
learning from small amounts of data. One of the most popular
and elegant Few-Shot learning approaches is Model-Agnostic
Meta-Learning (MAML). In this paper, we propose a novel
framework for Bayesian MAML called BH-MAML, which
employs Hypernetworks for weight updates. It learns the
universal weights point-wise, but a probabilistic structure is
added when adapted for specific tasks. In such a framework,
we can use simple Gaussian distributions or more complicated posteriors induced by Continuous Normalizing Flows.
Automating Thought of Search: A Journey Towards Soundness and Completeness (Student Abstract)
Authors: Daniel Cao; Michael Katz; Harsha Kokel; Kavitha Srinivas; Shirin Sohrabi
Venue: AAAI 2025 (main)

Abstract: Large language models (LLMs) now turn their attention to search. Recently, Thought of Search (ToS) proposed defining the search space with code, having an LLM produce that code. ToS requires a human in the loop, collaboratively producing a sound successor function and goal test, achieving impressive 100% accuracy on all the tested datasets. In this work, we automate ToS (AutoToS), completely taking the human out of the loop of solving planning problems. AutoToS guides the language model step by step towards the generation of sound and complete search components, through feedback from both generic and domain specific unit tests. We achieve 100% accuracy, with minimal feedback iterations, using LLMs of various sizes on all evaluated domains.
Communication Accommodation Between Large Language Models and Users Across Cultures (Student Abstract)
Authors: Rong-Ching Chang; Hao-Chuan Wang
Venue: AAAI 2025 (main)

Abstract: The increasing adoption of conversational agents powered by large language models (LLMs) raises questions about its effects across culturally diverse interactions. While these agents are linguistically versatile and multilingual, their ability to adapt along cultural dimensions--defined as geographically and communally nurtured sets of values and behavioral norms--lacks close scrutiny of both their design and deployment. To achieve inclusive conversational AI, it is essential to understand how agents adapt to users from diverse cultural backgrounds. In this study, we analyze dialogues between human users from different countries and LLM-powered agents to examine how both parties adapt their word use, a salient aspect of linguistic styles, toward one another throughout casual conversations. Our analysis reveals that LLMs exhibit varying degrees of style matching based on users' national cultures and demonstrate asymmetric adaptation when interacting with culturally diverse users. Moreover, we observe a reciprocal dynamic where both the LLMs and users from certain cultures adjust their styles in response to one another. Additionally, our findings support the hypothesis that LLMs and users naturally converge in conversational styles over the course of interactions, mirroring the dynamics of human conversations that accommodate and converge. 
To develop localized and culturally aware agents, there's a potential to utilize such cross-cultural convergence process during fine-tuning to align LLMs.
scMBERT: A Pre-Trained Deep Learning Model for Single-Cell Multiomic Data Representation and Prediction (Student Abstract)
Authors: Xiaojian Chen; Kuai Yu; Min-Zhi Jiang; Cihan Xiao; Ziqi Fu; Weiqiang Zhou
Venue: AAAI 2025 (main)

Abstract: Recent advancements in single-cell sequencing technologies enable the measurement of multiple modalities in individual cells, offering insights into the transcriptome and regulome in various biological systems and human diseases in an unprecedented resolution. However, effectively using these ultra-high-dimensional and large-scale multiomic data to understand gene regulation remains challenging. Inspired by the success of adapting large language models into the genomics field, we develop scMBERT, a BERT framework-based pre-trained deep learning model using single-cell multiomic data. We showed that scMBERT increases model flexibility and performance in downstream tasks like cell type annotation and batch-effect correction, demonstrating the potential of leveraging multiomic data to improve single-cell genomic data analyses.
Automatic Summarization of Long Documents (Student Abstract)
Authors: Naman Chhibbar; Jugal Kalita
Venue: AAAI 2025 (main)

Abstract: A vast amount of textual data is added to the internet daily, making utilization and interpretation of textual data difficult and cumbersome.
As a result, automatic text summarization is crucial for extracting relevant information, saving precious time.
Although many transformer models excel in summarization, they are constrained by their input size, preventing them from processing texts longer than their context size.
This study introduces several novel algorithms that allow any LLM to efficiently overcome its input size limitation, effectively utilizing its full potential without any architectural modifications.
We test our algorithms on texts with more than 70,000 words, and our experiments show a significant increase in BERTScore with competitive ROUGE scores.
Optimized Random Features for the Neural Tangent Kernel (Student Abstract)
Authors: Shrutimoy Das; Binita Maity
Venue: AAAI 2025 (main)

Abstract: The neural tangent kernel (NTK) has emerged as an important tool in recent years, both for developing a theoretical understanding of deep learning as well as for various applications. Even though  recursive closed form expressions have been derived for computing the NTK, these become computationally expensive as the complexity of  a network increases. Recent papers have looked at reducing this complexity using various sketching techniques along with random features. Building on these techniques, we propose an additional optimization step which results in better approximation of the NTK.
Scaling Effects on Latent Representation Edits in GPT Models (Student Abstract)
Authors: Austin L. Davis; Gita Sukthankar
Venue: AAAI 2025 (main)

Abstract: Probing classifiers are a technique for understanding and modifying the operation of neural networks in which a smaller classifier is trained to use the model's internal representation to learn a related probing task.  Similar to a neural electrode array, training probing classifiers can help researchers both discern and edit the internal representation of a neural network.  This paper presents an evaluation of the use of probing classifiers to modify the internal hidden state of a chess-playing transformer. 
We demonstrate that intervention vector scaling should follow a negative exponential according to the length of the input to ensure model outputs remain semantically valid after editing the residual stream activations.
QAagent: A Multiagent System for Unit Test Generation via Natural Language Pseudocode (Student Abstract)
Authors: Akhil Deo
Venue: AAAI 2025 (main)

Abstract: Unit testing is essential for ensuring software quality, but it is often time-consuming and prone to developer oversight. With the rise of large language models (LLMs) in code generation, there is an increasing need for reliable and automated test generation systems. This work presents QAagent, a multi-agent system designed to generate unit tests using natural language pseudocode. QAagent leverages LLMs to create a detailed natural language plan of a function's implementation and then generates a comprehensive suite of test cases covering both base and edge scenarios.  Experiments conducted on two widely-used benchmarks, HumanEval and MBPP, show that QAagent consistently outperforms existing frameworks in terms of code coverage, although its accuracy varies across datasets, demonstrating the potential for utilizing natural language pseudocode to to enhance automated test generation in LLM-driven coding environments.
AoI-MDP: An AoI Optimized Markov Decision Process Dedicated in the Underwater Task (Student Abstract)
Authors: Yimian Ding; Jingzehua Xu; Yiyuan Yang; Guanwen Xie; Xinqi Wang; Shuai Zhang
Venue: AAAI 2025 (main)

Abstract: Ocean exploration places high demands on autonomous underwater vehicles, especially when there's observation delay. We propose age of information optimized Markov decision process (AoI-MDP) to enhance underwater tasks by modeling observation delay as signal delay and including it in the state space. AoI-MDP also introduces wait time in the action space and integrates AoI with reward functions, optimizing information freshness and decision-making using reinforcement learning. Simulations show AoI-MDP outperforms the standard MDP, demonstrating superior performance, feasibility, and generalization in underwater tasks. To accelerate relevant research, we have made the codes available as open-source at https://github.com/Xiboxtg/AoI-MDP.
Leveraging Textual Memory and Key Frame Reasoning for Full Video Understanding Using Off-the-Shelf LLMs and VLMs (Student Abstract)
Authors: Harsh Dubey; Chulwoo Pack
Venue: AAAI 2025 (main)

Abstract: To address the limitations of current Large-scale Video-Language Models (LVLMs) in fine-grained understanding and long-term temporal memory, we propose a novel video understanding approach that integrates a Vision Language Model (VLM) and a Large Language Model (LLM) with a textual memory mechanism to ensure continuity and contextual coherence. In addition, we introduce a novel evaluation metric, VAD-Score (Video Automated Description Score), to assess precision, recall, and F1 scores for events, subjects, and objects. Our approach delivers competitive results on a diverse set of videos from the DREAM-1K dataset, spanning categories such as live-action, animation, shorts, stock, and YouTube, with a focus on fine-grained comprehension.
All You Need Is S P A C E: When Jailbreaking Meets Bias Audit and Reveals What Lies Beneath the Guardrails (Student Abstract)
Authors: Arka Dutta; Aman Priyanshu; Ashiqur R. KhudaBukhsh
Venue: AAAI 2025 (main)

Abstract: This paper makes a novel combination of a recently proposed bias audit framework and a recently proposed jailbreaking technique for Llama3. On an audit comprising several disadvantaged groups, our experiments reveal that a jailbroken Llama3 exhibits worrisome antisemitism, racism, misogyny, and homophobia (to list a few) much akin to a broad suite of LLMs that were susceptible to similar biases.
ARTICLE: Annotator Reliability Through In-Context Learning (Student Abstract)
Authors: Sujan Dutta; Deepak Pandita; Tharindu Cyril Weerasooriya; Marcos Zampieri; Christopher M. Homan; Ashiqur R. KhudaBukhsh
Venue: AAAI 2025 (main)

Abstract: Ensuring annotator quality in training and evaluation data is a key piece of machine learning in NLP. Tasks such as sentiment analysis and offensive speech detection are intrinsically subjective, creating a challenging scenario for traditional quality assessment approaches because it is hard to distinguish disagreement due to poor work from that due to differences of opinions between sincere annotators. With the goal of increasing diverse perspectives in annotation while ensuring consistency, we propose ARTICLE, an in-context learning (ICL) framework to estimate annotation quality through self-consistency. We evaluate this framework on two offensive speech datasets using multiple LLMs and compare its performance with traditional methods. Our findings indicate that ARTICLE can be used as a robust method for identifying reliable annotators, hence improving data quality.
On-Policy Algorithms for Continual Reinforcement Learning (Student Abstract)
Authors: Tadeusz Dziarmaga; Tomasz Arczewski; Marcin Mazur; Maciej Wołczyk
Venue: AAAI 2025 (main)

Abstract: Continual reinforcement learning (CRL) is the study of optimal strategies for maximizing rewards in sequential environments that change over time. This is particularly crucial in domains such as robotics, where the operational environment is inherently dynamic and subject to continual change. Nevertheless, research in this area has thus far concentrated on off-policy algorithms with replay buffers that are capable of amortizing the impact of distribution shifts. Such an approach is not feasible with on-policy reinforcement learning algorithms that learn solely from the data obtained from the current policy. In this paper, we examine the performance of proximal policy optimization (PPO), a prevalent on-policy reinforcement learning (RL) algorithm, in a classical CRL benchmark. Our findings suggest that the current methods are suboptimal in terms of average performance. Nevertheless, they demonstrate encouraging competitive outcomes with respect to forward transfer and forgetting metrics. This highlights the need for further research into continual on-policy reinforcement learning. The source code is available at https://github.com/Teddy298/continualworld-ppo.
Augmented Lagrangian Risk-constrained Reinforcement Learning for Portfolio Optimization (Student Abstract)
Authors: Bayaraa Enkhsaikhan; Ohyun Jo
Venue: AAAI 2025 (main)

Abstract: We applied Risk-averse Reinforcement Learning (RL) to optimize investment portfolios while incorporating risk constraints. Given that portfolios must adhere to risk constraints set by investors and regulators, enforcing hard constraints is essential for practical portfolio optimization. Traditional techniques often lack the flexibility to model the complexities of dynamic financial markets. To address this, we used the Augmented Lagrangian Multiplier (ALM) to impose constraints on the agent, reducing risk during decision-making. Our risk-constrained RL algorithm demonstrated no constraint violations during testing and outperformed other Risk-averse RL methods, indicating its potential for optimizing portfolios for risk-averse investors.
Accounting for Spatial Variability with the Histogram of Oriented Gradients Based Masking Improves Performance of Masked Autoencoder over Hyperspectral Satellite Imagery (Student Abstract)
Authors: Tanjim Bin Faruk; Abdul Matin; Shrideep Pallickara; Sangmi Lee Pallickara
Venue: AAAI 2025 (main)

Abstract: Masked autoencoders employ random masking to effectively reconstruct input images using self-supervised techniques, which allows for efficient training on large datasets. However, the random masking strategy does not adequately tap into information encapsulated within high-dimensional hyperspectral satellite imagery that is used in several domains. We propose a novel masking strategy, HOGMAE, based on the Histogram of Oriented Gradients that incorporates rich information inherent within satellite images during the mask creation step. Our experiments, over a hyperspectral satellite dataset, demonstrate the effectiveness of our methodology.
Multi-Modal Hand-to-Mouth Gesture Recognition in Activity-Oriented RGB-Thermal Footage (Student Abstract)
Authors: Glenn Fernandes; Meixi Lu; Farzad Shahabi; Jiayi Zheng; Aggelos Katsaggelos; Nabil Alshurafa
Venue: AAAI 2025 (main)

Abstract: Health-risk behaviors such as overeating and smoking have a profound impact on public health, making their monitoring and mitigation critical. Wearable RGB-Thermal cameras are being employed to monitor these behaviors by capturing hand-to-mouth (HTM) gestures, which are central to them. However, detection models relying on single modalities—either RGB or thermal—often struggle to accurately distinguish these confounding gestures due to inherent sensor limitations, such as sensitivity to lighting conditions or thermal occlusions. We present a family of fusion models that integrate RGB and thermal video data using early-, decision- , and a novel mid-fusion architecture, RGB-Thermal Fusion Video Network (RTFVNet), designed to enhance the recognition of HTM gestures associated with eating and smoking. Our evaluation shows that while decision fusion achieves the highest F1-score of 88% (0.44 TFLOPs), RTFVNet offers an optimal balance between performance (85%) and complexity (0.37 TFLOPs) for gesture classification of eating, smoking, and non-gesture activities.
Enhancing Aging Biomarker Research through Large Language Models and Knowledge Graphs (Student Abstract)
Authors: Srikar Reddy Gadusu; Yiğit Küçük; Vania Santillana; Aaron King; Hande Küçük McGinty
Venue: AAAI 2025 (main)

Abstract: Aging biomarkers play a crucial role in uncovering the biological mechanisms behind aging and in developing strategies to support healthy aging. However, the search for reliable aging biomarkers is particularly challenging due to the intricate and multifactorial nature of the aging process. Furthermore, biomarker names and categories are not well-standardized in the current literature. While, a formal definition of a biomarker is nonexistent in the current literature, formally defining biomarkers and standardizing the vocabulary for biomarkers can help accelerate AI research around this concept which can lead to better, faster and more accurate analyses of the existing data and literature.
Thus, in this work, we generated Knowledge Graphs that can help us define and standardize biomarkers. We present our Knowledge Graphs (KGs) generated using both an LLM and expert-curated datasets. We compare both KGs to understand why systematic integration between these two models is needed. The integration of Knowledge Graphs (KGs) and Large Language Models (LLMs) presents a promising approach to advancing aging biomarker research through the inherent structured and standardized nature of ontology schemas in knowledge graphs. We showcase that the accuracy of LLM-generated KGs remains questionable but systematic methods such as KNARM can help us with the accuracy of these efforts. In future work, we will propose a synergistic framework where KGs and LLMs interact iteratively to improve both the comprehensiveness and accuracy of aging biomarker information.
2-in-1 Phishing Detection via Large LM Distillation and Small LM Perturbation (Student Abstract)
Authors: Calvin Greenewald; Bradley Ashmore; Chien-Sing Poon; Lingwei Chen
Venue: AAAI 2025 (main)

Abstract: Phishing emails are an escalating threat, underscoring the need for precise detection methods. While large language models (LLMs) have gained attention for their potential in this area, their reliance on extensive data for fine-tuning poses practical challenges. This paper introduces DualLM for phishing detection with minimal data, which distills the reasoning ability from a large LM to enhance a small target LM and integrates trainable perturbations to improve the small LM's inference capabilities. Experiments demonstrate that DualLM can benefit from dual LMs, which reduces training parameters and data required, while maintaining high performance in phishing email detection with limited data.
Assessing Vulnerabilities in State-of-the-Art Large Language Models Through Hex Injection (Student Abstract)
Authors: Da Cheng Gu; Wei Liu
Venue: AAAI 2025 (main)

Abstract: State-of-the-art large language models (LLMs) are designed with robust safeguards to prevent the disclosure of harmful information and dangerous procedures. However, "jailbreaking" techniques can circumvent these protections by exploiting vulnerabilities in the models. This paper introduces a novel method, Hex Injection, which leverages a specific weakness in LLMs' ability to decode encoded text to uncover concealed dangerous instructions. Hex Injection distinguishes itself from traditional methods by combining encoded instructions with plaintext prompts to reveal unsafe content more effectively. Our approach involves encoding potentially malicious prompts in hexadecimal and integrating them. We observe a 94% average success rate (ASR) with a combination of plaintext, encoded, and role-play for Llama 3 and 3.1 models, and an 86% ASR for the Gemma 2 model. This research not only advances the understanding of LLM security but also offers valuable insights for improving safety mechanisms in artificial intelligence systems.